{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX A.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}22 Apr 2023, 09:45:29
{txt}
{com}. 
. 
. 
. 
. 
. 
. *** APPENDIX ANALYSES A: UNIVARIATE DISTIRBUTIONS OF PRRESIDENTIAL LOYALTY COVARIATE & ALTERNATIVE ESTIMATION APPROACHES ****
. 
. 
. 
. 
. ** RETRIEVE SINGLE EVENT RECORDS DATABASE [N = 860 APPOINTEE OBSERVATIONS: 831 UNCENSORED OBSERVATIONS; 29 CENSORED OBSERVATIONS] **
. 
. use "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Data\Krause and Byers.SRD.06-03-2022.dta", replace
{txt}
{com}. 
. 
. *
. *
. 
. 
. ** GENERATE CENSORING VARIABLE FOR HOLDOVER APPOINTEES SERVING BETWEEN/ACROSS ADMINISTRATIONS [=1]; UNCENSRED OBSERVATIONS [=0] ** 
. 
. gen singleadmin_service=1 if holdover==0
{txt}(29 missing values generated)

{com}. *
. replace singleadmin_service=0 if holdover==1
{txt}(29 real changes made)

{com}. *
. *
. tab singleadmin_service

{txt}singleadmin {c |}
   _service {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         29        3.37        3.37
{txt}          1 {c |}{res}        831       96.63      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        860      100.00
{txt}
{com}. 
. 
. ** SET FOR SURVIVAL DATA WITH A SINGLE RECORD PER APPOINTEE OBSERVATION [N = 860: UNCENSORED N = 831; CENSORED N = 29] ** 
. stset okapptdur, failure(singleadmin_service)

     {txt}failure event:  {res}singleadmin_service != 0 & singleadmin_service < .
{txt}obs. time interval:  {res}(0, okapptdur]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}        860{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}        860{txt}  observations remaining, representing
{res}        831{txt}  failures in single-record/single-failure data
{res}    850,034{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}    4,074
{txt}
{com}. 
. 
. 
. 
. 
. 
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. *** APPENDIX ANALYSES A: UNIVARIATE DISTIRBUTIONS OF PRRESIDENTIAL LOYALTY COVARIATE & ALTERNATIVE ESTIMATION APPROACHES ****
. 
. *** FIGURE A0 ***
. 
. kdensity zloyalmedian, lcolor(black) addplot((kdensity zloyalmedian if soubinaryagency2nom==0, lcolor(gs6) lpattern(dash)) kdensity zloyalmedian if soubinaryagency2nom==1, lcolor(gs10) lpattern(longdash_dot)) xline(0, lcolor(red%40) lpattern(dash)) ylabel(0(0.2)0.8, angle(0) labsize(small)) xlabel(-2(1)3, angle(0) labsize(small)) note("") ytitle("Density", size(small) margin(r=2.5)) xtitle("Presidential Loyalty", size(small) margin(t=2)) title("FIGURE A0: Univariate Distributions of Presidential Loyalty Covariate", size(medsmall)) legend(order(1 "Presidential Loyalty (Full Sample)" 2 "Presidential Loyalty (Non-Policy Priority Agencies)" 3 "Presidential Loyalty (Policy Priority Agencies)") size(small))
{res}{txt}
{com}. 
. *
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureA0.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureA0.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureA0.gph saved)

{com}. 
. 
. 
. 
. 
. 
. *** APPENDIX ANALYSES A: UNIVARIATE DISTIRBUTIONS OF PRRESIDENTIAL LOYALTY COVARIATE & ALTERNATIVE ESTIMATION APPROACHES ****
. 
. 
. 
. 
. *** FIRST, BEGIN WITH MANUSCRIPT REPORTED MODELS 2 & 4 -- AND FIGURE 2 FOR THE GRAPHICAL PRESENTATION TO BE INCLUDED IN THE APPENDIX DOCUMENT
. 
. 
. 
. 
. **** MODEL 2: COX MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i. okstartadyr  i.sbagency reagan bush41 clinton bush43,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
Iteration 0:   log pseudolikelihood = {res}-4793.4442
{txt}Iteration 1:   log pseudolikelihood = {res}-4497.4067
{txt}Iteration 2:   log pseudolikelihood = {res}-4471.0956
{txt}Iteration 3:   log pseudolikelihood = {res}-4470.7443
{txt}Iteration 4:   log pseudolikelihood = {res}-4470.7439
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-4470.7439

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}40{txt})    =  {res}  93737.48
{txt}Log pseudolikelihood =   {res}-4470.7439             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.385689{col 48}{space 2} .1605757{col 59}{space 1}    2.81{col 68}{space 3}0.005{col 76}{space 4} 1.104148{col 89}{space 3} 1.739019
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.152291{col 48}{space 2} .1989596{col 59}{space 1}    0.82{col 68}{space 3}0.412{col 76}{space 4} .8214672{col 89}{space 3} 1.616346
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6214336{col 48}{space 2} .0871684{col 59}{space 1}   -3.39{col 68}{space 3}0.001{col 76}{space 4} .4720596{col 89}{space 3}  .818074
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.033714{col 48}{space 2} .0821645{col 59}{space 1}    0.42{col 68}{space 3}0.677{col 76}{space 4} .8845916{col 89}{space 3} 1.207975
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .9793862{col 48}{space 2} .0669574{col 59}{space 1}   -0.30{col 68}{space 3}0.761{col 76}{space 4} .8565646{col 89}{space 3} 1.119819
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .5103239{col 48}{space 2} .0549484{col 59}{space 1}   -6.25{col 68}{space 3}0.000{col 76}{space 4} .4132321{col 89}{space 3} .6302282
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .6508912{col 48}{space 2} .1725959{col 59}{space 1}   -1.62{col 68}{space 3}0.105{col 76}{space 4} .3870762{col 89}{space 3} 1.094512
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .6205378{col 48}{space 2} .1670667{col 59}{space 1}   -1.77{col 68}{space 3}0.076{col 76}{space 4} .3661004{col 89}{space 3} 1.051808
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.768227{col 48}{space 2} .3203508{col 59}{space 1}    3.15{col 68}{space 3}0.002{col 76}{space 4} 1.239725{col 89}{space 3} 2.522033
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 2.061222{col 48}{space 2} .6000132{col 59}{space 1}    2.48{col 68}{space 3}0.013{col 76}{space 4} 1.165047{col 89}{space 3} 3.646751
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 1.66e-11{col 48}{space 2} 1.76e-10{col 59}{space 1}   -2.34{col 68}{space 3}0.019{col 76}{space 4} 1.54e-20{col 89}{space 3} .0178679
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 1155.527{col 48}{space 2} 2625.988{col 59}{space 1}    3.10{col 68}{space 3}0.002{col 76}{space 4} 13.43959{col 89}{space 3} 99351.49
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1648507{col 48}{space 2} .0356675{col 59}{space 1}   -8.33{col 68}{space 3}0.000{col 76}{space 4} .1078755{col 89}{space 3} .2519177
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9897732{col 48}{space 2} .0046195{col 59}{space 1}   -2.20{col 68}{space 3}0.028{col 76}{space 4} .9807605{col 89}{space 3} .9988688
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 1.139291{col 48}{space 2} .0988381{col 59}{space 1}    1.50{col 68}{space 3}0.133{col 76}{space 4} .9611459{col 89}{space 3} 1.350454
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 1.620165{col 48}{space 2} .3677176{col 59}{space 1}    2.13{col 68}{space 3}0.034{col 76}{space 4} 1.038407{col 89}{space 3} 2.527845
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 3.954025{col 48}{space 2} .8766031{col 59}{space 1}    6.20{col 68}{space 3}0.000{col 76}{space 4} 2.560524{col 89}{space 3} 6.105902
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 3.535386{col 48}{space 2}  1.19626{col 59}{space 1}    3.73{col 68}{space 3}0.000{col 76}{space 4} 1.821452{col 89}{space 3} 6.862082
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.638361{col 48}{space 2} .4030985{col 59}{space 1}    2.01{col 68}{space 3}0.045{col 76}{space 4} 1.011537{col 89}{space 3}  2.65361
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 3.699317{col 48}{space 2} .9103052{col 59}{space 1}    5.32{col 68}{space 3}0.000{col 76}{space 4} 2.283826{col 89}{space 3} 5.992113
{txt}{space 32}7  {c |}{col 36}{res}{space 2}  5.65147{col 48}{space 2} 1.733666{col 59}{space 1}    5.65{col 68}{space 3}0.000{col 76}{space 4} 3.097731{col 89}{space 3} 10.31049
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 9.027404{col 48}{space 2} 3.467645{col 59}{space 1}    5.73{col 68}{space 3}0.000{col 76}{space 4} 4.252025{col 89}{space 3} 19.16594
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 3.268191{col 48}{space 2} .9402642{col 59}{space 1}    4.12{col 68}{space 3}0.000{col 76}{space 4} 1.859582{col 89}{space 3} 5.743804
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 2.070785{col 48}{space 2} .5439464{col 59}{space 1}    2.77{col 68}{space 3}0.006{col 76}{space 4} 1.237498{col 89}{space 3} 3.465179
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 1.353112{col 48}{space 2}  .322891{col 59}{space 1}    1.27{col 68}{space 3}0.205{col 76}{space 4} .8476432{col 89}{space 3} 2.160005
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.157977{col 48}{space 2} .3230165{col 59}{space 1}    0.53{col 68}{space 3}0.599{col 76}{space 4} .6702827{col 89}{space 3} 2.000514
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.909858{col 48}{space 2} .7065463{col 59}{space 1}    4.40{col 68}{space 3}0.000{col 76}{space 4} 1.807967{col 89}{space 3} 4.683313
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 1.994085{col 48}{space 2} .5981195{col 59}{space 1}    2.30{col 68}{space 3}0.021{col 76}{space 4} 1.107716{col 89}{space 3} 3.589706
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 2.711405{col 48}{space 2} .7702176{col 59}{space 1}    3.51{col 68}{space 3}0.000{col 76}{space 4} 1.553807{col 89}{space 3} 4.731422
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 2.476116{col 48}{space 2} .6707849{col 59}{space 1}    3.35{col 68}{space 3}0.001{col 76}{space 4} 1.456059{col 89}{space 3} 4.210787
{txt}{space 31}11  {c |}{col 36}{res}{space 2} 4.394187{col 48}{space 2} 1.419151{col 59}{space 1}    4.58{col 68}{space 3}0.000{col 76}{space 4} 2.333316{col 89}{space 3} 8.275293
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 1.853924{col 48}{space 2}  .336007{col 59}{space 1}    3.41{col 68}{space 3}0.001{col 76}{space 4} 1.299629{col 89}{space 3} 2.644628
{txt}{space 31}13  {c |}{col 36}{res}{space 2} 1.752547{col 48}{space 2} .4423016{col 59}{space 1}    2.22{col 68}{space 3}0.026{col 76}{space 4} 1.068677{col 89}{space 3} 2.874041
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 2.860757{col 48}{space 2} .8250644{col 59}{space 1}    3.64{col 68}{space 3}0.000{col 76}{space 4} 1.625504{col 89}{space 3} 5.034706
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 1.779291{col 48}{space 2} .4856656{col 59}{space 1}    2.11{col 68}{space 3}0.035{col 76}{space 4} 1.042096{col 89}{space 3}  3.03799
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .8685938{col 48}{space 2} .1319371{col 59}{space 1}   -0.93{col 68}{space 3}0.354{col 76}{space 4} .6449431{col 89}{space 3} 1.169801
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 1.631593{col 48}{space 2} .1207942{col 59}{space 1}    6.61{col 68}{space 3}0.000{col 76}{space 4} 1.411216{col 89}{space 3} 1.886384
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 2.208613{col 48}{space 2} .6426988{col 59}{space 1}    2.72{col 68}{space 3}0.006{col 76}{space 4} 1.248599{col 89}{space 3} 3.906756
{txt}{space 31}19  {c |}{col 36}{res}{space 2} .8145991{col 48}{space 2}  .127576{col 59}{space 1}   -1.31{col 68}{space 3}0.190{col 76}{space 4} .5992881{col 89}{space 3} 1.107267
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .2521271{col 48}{space 2} .0811474{col 59}{space 1}   -4.28{col 68}{space 3}0.000{col 76}{space 4} .1341711{col 89}{space 3} .4737834
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .8452074{col 48}{space 2} .0715444{col 59}{space 1}   -1.99{col 68}{space 3}0.047{col 76}{space 4} .7159974{col 89}{space 3} .9977347
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .4743225{col 48}{space 2} .1708597{col 59}{space 1}   -2.07{col 68}{space 3}0.038{col 76}{space 4}  .234129{col 89}{space 3} .9609311
{txt}{space 31}23  {c |}{col 36}{res}{space 2} 1.028681{col 48}{space 2} .2622634{col 59}{space 1}    0.11{col 68}{space 3}0.912{col 76}{space 4} .6241169{col 89}{space 3} 1.695491
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .3016769{col 48}{space 2} .1438047{col 59}{space 1}   -2.51{col 68}{space 3}0.012{col 76}{space 4} .1185188{col 89}{space 3} .7678859
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.429821{col 48}{space 2} .1932887{col 59}{space 1}    2.64{col 68}{space 3}0.008{col 76}{space 4} 1.097016{col 89}{space 3}  1.86359
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .7985169{col 48}{space 2} .1130966{col 59}{space 1}   -1.59{col 68}{space 3}0.112{col 76}{space 4} .6049586{col 89}{space 3} 1.054005
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} 1.566368{col 48}{space 2} .1479302{col 59}{space 1}    4.75{col 68}{space 3}0.000{col 76}{space 4} 1.301682{col 89}{space 3} 1.884875
{txt}{space 31}29  {c |}{col 36}{res}{space 2}  3.94769{col 48}{space 2} 1.373691{col 59}{space 1}    3.95{col 68}{space 3}0.000{col 76}{space 4}  1.99594{col 89}{space 3} 7.807978
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 1.607636{col 48}{space 2} .4787072{col 59}{space 1}    1.59{col 68}{space 3}0.111{col 76}{space 4}   .89686{col 89}{space 3} 2.881713
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 2.150532{col 48}{space 2} .4436304{col 59}{space 1}    3.71{col 68}{space 3}0.000{col 76}{space 4} 1.435333{col 89}{space 3} 3.222101
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 3.473497{col 48}{space 2} .9447425{col 59}{space 1}    4.58{col 68}{space 3}0.000{col 76}{space 4} 2.038224{col 89}{space 3} 5.919456
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.565314{col 48}{space 2} .5211938{col 59}{space 1}    1.35{col 68}{space 3}0.178{col 76}{space 4} .8150452{col 89}{space 3} 3.006223
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.513868{col 48}{space 2} .1622943{col 59}{space 1}    3.87{col 68}{space 3}0.000{col 76}{space 4} 1.226973{col 89}{space 3} 1.867846
{txt}{space 31}54  {c |}{col 36}{res}{space 2} 1.780839{col 48}{space 2} .3605465{col 59}{space 1}    2.85{col 68}{space 3}0.004{col 76}{space 4} 1.197544{col 89}{space 3} 2.648243
{txt}{space 31}55  {c |}{col 36}{res}{space 2} 1.279158{col 48}{space 2} .4553753{col 59}{space 1}    0.69{col 68}{space 3}0.489{col 76}{space 4} .6366486{col 89}{space 3} 2.570092
{txt}{space 31}56  {c |}{col 36}{res}{space 2}   1.0262{col 48}{space 2} .3879511{col 59}{space 1}    0.07{col 68}{space 3}0.945{col 76}{space 4} .4891462{col 89}{space 3} 2.152909
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 1.418405{col 48}{space 2} .4602064{col 59}{space 1}    1.08{col 68}{space 3}0.281{col 76}{space 4} .7509737{col 89}{space 3} 2.679017
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .3575611{col 48}{space 2} .1310554{col 59}{space 1}   -2.81{col 68}{space 3}0.005{col 76}{space 4} .1743262{col 89}{space 3} .7333945
{txt}{space 31}60  {c |}{col 36}{res}{space 2} 1.117945{col 48}{space 2} .1726301{col 59}{space 1}    0.72{col 68}{space 3}0.470{col 76}{space 4} .8259998{col 89}{space 3} 1.513076
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} .0558675{col 48}{space 2} .0531481{col 59}{space 1}   -3.03{col 68}{space 3}0.002{col 76}{space 4} .0086575{col 89}{space 3} .3605182
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2} .1506936{col 48}{space 2} .0925721{col 59}{space 1}   -3.08{col 68}{space 3}0.002{col 76}{space 4}  .045206{col 89}{space 3} .5023352
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} .6407045{col 48}{space 2} .3377118{col 59}{space 1}   -0.84{col 68}{space 3}0.398{col 76}{space 4} .2280311{col 89}{space 3} 1.800203
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2} .2117962{col 48}{space 2}  .155203{col 59}{space 1}   -2.12{col 68}{space 3}0.034{col 76}{space 4} .0503689{col 89}{space 3} .8905816
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-4793.444{col 39}-4470.744{col 50}    40{col 58} 9021.488{col 69} 9211.765
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. 
. *** COMPUTE Figure A1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQ = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5222964{col 26}{space 2} .1000269{col 37}{space 1}   -3.39{col 46}{space 3}0.001{col 54}{space 4} .3588396{col 67}{space 3} .7602102
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix model2zloyal = r(table)
{txt}
{com}. mat list model2zloyal
{res}
{txt}model2zloyal[9,1]
               (1)
     b {res}  .52229641
{txt}    se {res}  .10002687
{txt}     z {res} -3.3915086
{txt}pvalue {res}  .00069509
{txt}    ll {res}  .35883961
{txt}    ul {res}  .76021023
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. **** COMPUTE Figure A2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. 
. 
. **** MODEL 4: WEIBULL MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i. okstartadyr  i.sbagency reagan bush41 clinton bush43, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-604.21225}  
Iteration 2:{space 3}log pseudolikelihood = {res:-499.15183}  
Iteration 3:{space 3}log pseudolikelihood = {res:-497.85317}  
Iteration 4:{space 3}log pseudolikelihood = {res:-497.85043}  
Iteration 5:{space 3}log pseudolikelihood = {res:-497.85043}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(22)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-497.85043             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.375024{col 48}{space 2} .1587628{col 59}{space 1}    2.76{col 68}{space 3}0.006{col 76}{space 4} 1.096551{col 89}{space 3} 1.724215
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.153634{col 48}{space 2}  .205655{col 59}{space 1}    0.80{col 68}{space 3}0.423{col 76}{space 4} .8134419{col 89}{space 3} 1.636098
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6306902{col 48}{space 2} .0869891{col 59}{space 1}   -3.34{col 68}{space 3}0.001{col 76}{space 4} .4812963{col 89}{space 3} .8264557
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.041572{col 48}{space 2} .0817388{col 59}{space 1}    0.52{col 68}{space 3}0.604{col 76}{space 4} .8930797{col 89}{space 3} 1.214755
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .9846415{col 48}{space 2}  .065965{col 59}{space 1}   -0.23{col 68}{space 3}0.817{col 76}{space 4}  .863481{col 89}{space 3} 1.122803
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .5394921{col 48}{space 2} .0567041{col 59}{space 1}   -5.87{col 68}{space 3}0.000{col 76}{space 4} .4390544{col 89}{space 3} .6629058
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .7369114{col 48}{space 2} .1849133{col 59}{space 1}   -1.22{col 68}{space 3}0.224{col 76}{space 4} .4506331{col 89}{space 3} 1.205057
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .6935274{col 48}{space 2} .1764129{col 59}{space 1}   -1.44{col 68}{space 3}0.150{col 76}{space 4}  .421253{col 89}{space 3} 1.141785
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.685222{col 48}{space 2} .2970439{col 59}{space 1}    2.96{col 68}{space 3}0.003{col 76}{space 4} 1.192946{col 89}{space 3} 2.380637
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 1.743278{col 48}{space 2} .4804177{col 59}{space 1}    2.02{col 68}{space 3}0.044{col 76}{space 4} 1.015757{col 89}{space 3} 2.991874
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 7.77e-11{col 48}{space 2} 8.13e-10{col 59}{space 1}   -2.23{col 68}{space 3}0.026{col 76}{space 4} 9.78e-20{col 89}{space 3} .0617804
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 893.7962{col 48}{space 2} 2000.161{col 59}{space 1}    3.04{col 68}{space 3}0.002{col 76}{space 4} 11.12747{col 89}{space 3} 71792.74
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1747373{col 48}{space 2}  .037021{col 59}{space 1}   -8.23{col 68}{space 3}0.000{col 76}{space 4} .1153572{col 89}{space 3} .2646835
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9902345{col 48}{space 2} .0045307{col 59}{space 1}   -2.14{col 68}{space 3}0.032{col 76}{space 4} .9813942{col 89}{space 3} .9991544
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 1.130022{col 48}{space 2} .0980922{col 59}{space 1}    1.41{col 68}{space 3}0.159{col 76}{space 4} .9532305{col 89}{space 3} 1.339603
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 1.653304{col 48}{space 2} .3679208{col 59}{space 1}    2.26{col 68}{space 3}0.024{col 76}{space 4}  1.06888{col 89}{space 3} 2.557268
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 4.383809{col 48}{space 2} .9244283{col 59}{space 1}    7.01{col 68}{space 3}0.000{col 76}{space 4} 2.899719{col 89}{space 3} 6.627463
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 3.942686{col 48}{space 2} 1.239194{col 59}{space 1}    4.36{col 68}{space 3}0.000{col 76}{space 4} 2.129403{col 89}{space 3} 7.300061
{txt}{space 32}5  {c |}{col 36}{res}{space 2}  1.52843{col 48}{space 2}  .378723{col 59}{space 1}    1.71{col 68}{space 3}0.087{col 76}{space 4} .9404376{col 89}{space 3} 2.484054
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 3.451085{col 48}{space 2} .8518352{col 59}{space 1}    5.02{col 68}{space 3}0.000{col 76}{space 4} 2.127417{col 89}{space 3} 5.598333
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 6.295532{col 48}{space 2} 1.865565{col 59}{space 1}    6.21{col 68}{space 3}0.000{col 76}{space 4} 3.522043{col 89}{space 3} 11.25305
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 10.02152{col 48}{space 2} 3.814879{col 59}{space 1}    6.05{col 68}{space 3}0.000{col 76}{space 4} 4.752342{col 89}{space 3} 21.13291
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 2.832157{col 48}{space 2}  .761487{col 59}{space 1}    3.87{col 68}{space 3}0.000{col 76}{space 4} 1.672065{col 89}{space 3} 4.797128
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 1.808613{col 48}{space 2} .4552409{col 59}{space 1}    2.35{col 68}{space 3}0.019{col 76}{space 4} 1.104313{col 89}{space 3} 2.962097
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 1.234729{col 48}{space 2} .2752436{col 59}{space 1}    0.95{col 68}{space 3}0.344{col 76}{space 4} .7976698{col 89}{space 3} 1.911261
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.041834{col 48}{space 2} .2765581{col 59}{space 1}    0.15{col 68}{space 3}0.877{col 76}{space 4} .6192193{col 89}{space 3} 1.752882
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.509995{col 48}{space 2} .5911122{col 59}{space 1}    3.91{col 68}{space 3}0.000{col 76}{space 4}  1.58202{col 89}{space 3} 3.982297
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 1.786664{col 48}{space 2} .5124548{col 59}{space 1}    2.02{col 68}{space 3}0.043{col 76}{space 4} 1.018356{col 89}{space 3} 3.134631
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 2.373431{col 48}{space 2} .6278618{col 59}{space 1}    3.27{col 68}{space 3}0.001{col 76}{space 4} 1.413194{col 89}{space 3}  3.98613
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 2.225332{col 48}{space 2} .5587987{col 59}{space 1}    3.19{col 68}{space 3}0.001{col 76}{space 4} 1.360352{col 89}{space 3}  3.64031
{txt}{space 31}11  {c |}{col 36}{res}{space 2} 3.703864{col 48}{space 2} 1.144586{col 59}{space 1}    4.24{col 68}{space 3}0.000{col 76}{space 4} 2.021215{col 89}{space 3} 6.787308
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 1.709868{col 48}{space 2}  .290355{col 59}{space 1}    3.16{col 68}{space 3}0.002{col 76}{space 4} 1.225798{col 89}{space 3} 2.385099
{txt}{space 31}13  {c |}{col 36}{res}{space 2} 1.544219{col 48}{space 2} .3641007{col 59}{space 1}    1.84{col 68}{space 3}0.065{col 76}{space 4} .9727696{col 89}{space 3} 2.451364
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 2.418647{col 48}{space 2} .6620859{col 59}{space 1}    3.23{col 68}{space 3}0.001{col 76}{space 4} 1.414368{col 89}{space 3} 4.136017
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 1.618903{col 48}{space 2} .4115568{col 59}{space 1}    1.90{col 68}{space 3}0.058{col 76}{space 4} .9836234{col 89}{space 3} 2.664483
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .8490932{col 48}{space 2} .1381262{col 59}{space 1}   -1.01{col 68}{space 3}0.315{col 76}{space 4} .6172857{col 89}{space 3} 1.167951
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 1.615731{col 48}{space 2} .1250727{col 59}{space 1}    6.20{col 68}{space 3}0.000{col 76}{space 4} 1.388283{col 89}{space 3} 1.880442
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 1.938672{col 48}{space 2} .5299753{col 59}{space 1}    2.42{col 68}{space 3}0.015{col 76}{space 4} 1.134517{col 89}{space 3} 3.312817
{txt}{space 31}19  {c |}{col 36}{res}{space 2} .8042553{col 48}{space 2} .1243205{col 59}{space 1}   -1.41{col 68}{space 3}0.159{col 76}{space 4} .5940412{col 89}{space 3} 1.088858
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .3030017{col 48}{space 2} .0883753{col 59}{space 1}   -4.09{col 68}{space 3}0.000{col 76}{space 4} .1710718{col 89}{space 3} .5366754
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .8814764{col 48}{space 2} .0799942{col 59}{space 1}   -1.39{col 68}{space 3}0.164{col 76}{space 4}  .737843{col 89}{space 3}  1.05307
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .5227794{col 48}{space 2} .1752233{col 59}{space 1}   -1.94{col 68}{space 3}0.053{col 76}{space 4} .2710291{col 89}{space 3} 1.008373
{txt}{space 31}23  {c |}{col 36}{res}{space 2}  1.17553{col 48}{space 2} .2936578{col 59}{space 1}    0.65{col 68}{space 3}0.517{col 76}{space 4} .7204371{col 89}{space 3} 1.918101
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .3410356{col 48}{space 2} .1339979{col 59}{space 1}   -2.74{col 68}{space 3}0.006{col 76}{space 4} .1578883{col 89}{space 3} .7366299
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.502269{col 48}{space 2} .2167586{col 59}{space 1}    2.82{col 68}{space 3}0.005{col 76}{space 4} 1.132218{col 89}{space 3} 1.993267
{txt}{space 31}26  {c |}{col 36}{res}{space 2}   .80574{col 48}{space 2} .1241772{col 59}{space 1}   -1.40{col 68}{space 3}0.161{col 76}{space 4} .5956778{col 89}{space 3} 1.089879
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} 1.381344{col 48}{space 2} .1321202{col 59}{space 1}    3.38{col 68}{space 3}0.001{col 76}{space 4} 1.145217{col 89}{space 3} 1.666157
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 3.335883{col 48}{space 2} 1.073496{col 59}{space 1}    3.74{col 68}{space 3}0.000{col 76}{space 4} 1.775383{col 89}{space 3} 6.268009
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 1.401228{col 48}{space 2} .4054011{col 59}{space 1}    1.17{col 68}{space 3}0.244{col 76}{space 4} .7947687{col 89}{space 3} 2.470456
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 1.941637{col 48}{space 2} .3765196{col 59}{space 1}    3.42{col 68}{space 3}0.001{col 76}{space 4} 1.327713{col 89}{space 3} 2.839435
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 3.049867{col 48}{space 2}  .774135{col 59}{space 1}    4.39{col 68}{space 3}0.000{col 76}{space 4} 1.854487{col 89}{space 3} 5.015772
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.571156{col 48}{space 2} .5056538{col 59}{space 1}    1.40{col 68}{space 3}0.160{col 76}{space 4} .8361271{col 89}{space 3} 2.952339
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.501159{col 48}{space 2} .1594852{col 59}{space 1}    3.82{col 68}{space 3}0.000{col 76}{space 4} 1.218973{col 89}{space 3} 1.848671
{txt}{space 31}54  {c |}{col 36}{res}{space 2} 1.599048{col 48}{space 2} .3037601{col 59}{space 1}    2.47{col 68}{space 3}0.013{col 76}{space 4} 1.101957{col 89}{space 3} 2.320376
{txt}{space 31}55  {c |}{col 36}{res}{space 2} 1.062978{col 48}{space 2} .3619615{col 59}{space 1}    0.18{col 68}{space 3}0.858{col 76}{space 4}  .545351{col 89}{space 3} 2.071917
{txt}{space 31}56  {c |}{col 36}{res}{space 2} .9789283{col 48}{space 2}  .357858{col 59}{space 1}   -0.06{col 68}{space 3}0.954{col 76}{space 4} .4781728{col 89}{space 3} 2.004089
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 1.157172{col 48}{space 2} .3752958{col 59}{space 1}    0.45{col 68}{space 3}0.653{col 76}{space 4} .6128225{col 89}{space 3} 2.185047
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .3777419{col 48}{space 2} .0908125{col 59}{space 1}   -4.05{col 68}{space 3}0.000{col 76}{space 4} .2358081{col 89}{space 3} .6051062
{txt}{space 31}60  {c |}{col 36}{res}{space 2} .9463974{col 48}{space 2} .1372484{col 59}{space 1}   -0.38{col 68}{space 3}0.704{col 76}{space 4} .7122471{col 89}{space 3} 1.257524
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} .0624967{col 48}{space 2} .0587982{col 59}{space 1}   -2.95{col 68}{space 3}0.003{col 76}{space 4} .0098862{col 89}{space 3} .3950805
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2} .1549419{col 48}{space 2} .0945989{col 59}{space 1}   -3.05{col 68}{space 3}0.002{col 76}{space 4} .0468244{col 89}{space 3} .5127019
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} .6171753{col 48}{space 2}  .330683{col 59}{space 1}   -0.90{col 68}{space 3}0.368{col 76}{space 4} .2159405{col 89}{space 3} 1.763936
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2} .2147036{col 48}{space 2} .1571096{col 59}{space 1}   -2.10{col 68}{space 3}0.036{col 76}{space 4} .0511648{col 89}{space 3} .9009633
{txt}{space 29}_cons {c |}{col 36}{res}{space 2}  .000333{col 48}{space 2} .0018214{col 59}{space 1}   -1.46{col 68}{space 3}0.143{col 76}{space 4} 7.35e-09{col 89}{space 3} 15.09087
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} .9878179{col 48}{space 2} .0303399{col 59}{space 1}   32.56{col 68}{space 3}0.000{col 76}{space 4} .9283529{col 89}{space 3} 1.047283
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 2.685368{col 48}{space 2} .0814737{col 76}{space 4} 2.530338{col 89}{space 3} 2.849897
{txt}                               1/p {c |}{col 36}{res}{space 2} .3723884{col 48}{space 2} .0112982{col 76}{space 4} .3508898{col 89}{space 3} .3952041
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-830.8551{col 39}-497.8504{col 50}    24{col 58} 1043.701{col 69} 1157.867
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. 
. 
. *** COMPUTE Figure A1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQ = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5329473{col 26}{space 2} .1003618{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4} .3684601{col 67}{space 3} .7708644
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix model4zloyal = r(table)
{txt}
{com}. mat list model4zloyal
{res}
{txt}model4zloyal[9,1]
               (1)
     b {res}  .53294727
{txt}    se {res}  .10036181
{txt}     z {res} -3.3419205
{txt}pvalue {res}  .00083201
{txt}    ll {res}   .3684601
{txt}    ul {res}  .77086444
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. 
. **** COMPUTE Figure A2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. ** Generate 'manual' interaction variable ** 
. generate loyalppdiff = soubinaryagency2nom*zloyalmedian
{txt}
{com}. 
. ** Re-Estimate Model 4  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.sbagency reagan bush41 clinton bush43, distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-604.21225}  
Iteration 2:{space 3}log pseudolikelihood = {res:-499.15183}  
Iteration 3:{space 3}log pseudolikelihood = {res:-497.85317}  
Iteration 4:{space 3}log pseudolikelihood = {res:-497.85043}  
Iteration 5:{space 3}log pseudolikelihood = {res:-497.85043}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(22)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-497.85043             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.375024{col 40}{space 2} .1587628{col 51}{space 1}    2.76{col 60}{space 3}0.006{col 68}{space 4} 1.096551{col 81}{space 3} 1.724215
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.153634{col 40}{space 2}  .205655{col 51}{space 1}    0.80{col 60}{space 3}0.423{col 68}{space 4} .8134419{col 81}{space 3} 1.636098
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6306902{col 40}{space 2} .0869891{col 51}{space 1}   -3.34{col 60}{space 3}0.001{col 68}{space 4} .4812963{col 81}{space 3} .8264557
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.041572{col 40}{space 2} .0817388{col 51}{space 1}    0.52{col 60}{space 3}0.604{col 68}{space 4} .8930797{col 81}{space 3} 1.214755
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .9846415{col 40}{space 2}  .065965{col 51}{space 1}   -0.23{col 60}{space 3}0.817{col 68}{space 4}  .863481{col 81}{space 3} 1.122803
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5394921{col 40}{space 2} .0567041{col 51}{space 1}   -5.87{col 60}{space 3}0.000{col 68}{space 4} .4390544{col 81}{space 3} .6629058
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} .7369114{col 40}{space 2} .1849133{col 51}{space 1}   -1.22{col 60}{space 3}0.224{col 68}{space 4} .4506331{col 81}{space 3} 1.205057
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} .6935274{col 40}{space 2} .1764129{col 51}{space 1}   -1.44{col 60}{space 3}0.150{col 68}{space 4}  .421253{col 81}{space 3} 1.141785
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.685222{col 40}{space 2} .2970439{col 51}{space 1}    2.96{col 60}{space 3}0.003{col 68}{space 4} 1.192946{col 81}{space 3} 2.380637
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} 1.743278{col 40}{space 2} .4804177{col 51}{space 1}    2.02{col 60}{space 3}0.044{col 68}{space 4} 1.015757{col 81}{space 3} 2.991874
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 7.77e-11{col 40}{space 2} 8.13e-10{col 51}{space 1}   -2.23{col 60}{space 3}0.026{col 68}{space 4} 9.78e-20{col 81}{space 3} .0617804
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 893.7962{col 40}{space 2} 2000.161{col 51}{space 1}    3.04{col 60}{space 3}0.002{col 68}{space 4} 11.12747{col 81}{space 3} 71792.74
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .1747373{col 40}{space 2}  .037021{col 51}{space 1}   -8.23{col 60}{space 3}0.000{col 68}{space 4} .1153572{col 81}{space 3} .2646835
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9902345{col 40}{space 2} .0045307{col 51}{space 1}   -2.14{col 60}{space 3}0.032{col 68}{space 4} .9813942{col 81}{space 3} .9991544
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} 1.130022{col 40}{space 2} .0980922{col 51}{space 1}    1.41{col 60}{space 3}0.159{col 68}{space 4} .9532305{col 81}{space 3} 1.339603
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.653304{col 40}{space 2} .3679208{col 51}{space 1}    2.26{col 60}{space 3}0.024{col 68}{space 4}  1.06888{col 81}{space 3} 2.557268
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 4.383809{col 40}{space 2} .9244283{col 51}{space 1}    7.01{col 60}{space 3}0.000{col 68}{space 4} 2.899719{col 81}{space 3} 6.627463
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 3.942686{col 40}{space 2} 1.239194{col 51}{space 1}    4.36{col 60}{space 3}0.000{col 68}{space 4} 2.129403{col 81}{space 3} 7.300061
{txt}{space 24}5  {c |}{col 28}{res}{space 2}  1.52843{col 40}{space 2}  .378723{col 51}{space 1}    1.71{col 60}{space 3}0.087{col 68}{space 4} .9404376{col 81}{space 3} 2.484054
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 3.451085{col 40}{space 2} .8518352{col 51}{space 1}    5.02{col 60}{space 3}0.000{col 68}{space 4} 2.127417{col 81}{space 3} 5.598333
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 6.295532{col 40}{space 2} 1.865565{col 51}{space 1}    6.21{col 60}{space 3}0.000{col 68}{space 4} 3.522043{col 81}{space 3} 11.25305
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 10.02152{col 40}{space 2} 3.814879{col 51}{space 1}    6.05{col 60}{space 3}0.000{col 68}{space 4} 4.752342{col 81}{space 3} 21.13291
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 2.832157{col 40}{space 2}  .761487{col 51}{space 1}    3.87{col 60}{space 3}0.000{col 68}{space 4} 1.672065{col 81}{space 3} 4.797128
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 1.808613{col 40}{space 2} .4552409{col 51}{space 1}    2.35{col 60}{space 3}0.019{col 68}{space 4} 1.104313{col 81}{space 3} 2.962097
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 1.234729{col 40}{space 2} .2752436{col 51}{space 1}    0.95{col 60}{space 3}0.344{col 68}{space 4} .7976698{col 81}{space 3} 1.911261
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.041834{col 40}{space 2} .2765581{col 51}{space 1}    0.15{col 60}{space 3}0.877{col 68}{space 4} .6192193{col 81}{space 3} 1.752882
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.509995{col 40}{space 2} .5911122{col 51}{space 1}    3.91{col 60}{space 3}0.000{col 68}{space 4}  1.58202{col 81}{space 3} 3.982297
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 1.786664{col 40}{space 2} .5124548{col 51}{space 1}    2.02{col 60}{space 3}0.043{col 68}{space 4} 1.018356{col 81}{space 3} 3.134631
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 2.373431{col 40}{space 2} .6278618{col 51}{space 1}    3.27{col 60}{space 3}0.001{col 68}{space 4} 1.413194{col 81}{space 3}  3.98613
{txt}{space 24}9  {c |}{col 28}{res}{space 2} 2.225332{col 40}{space 2} .5587987{col 51}{space 1}    3.19{col 60}{space 3}0.001{col 68}{space 4} 1.360352{col 81}{space 3}  3.64031
{txt}{space 23}11  {c |}{col 28}{res}{space 2} 3.703864{col 40}{space 2} 1.144586{col 51}{space 1}    4.24{col 60}{space 3}0.000{col 68}{space 4} 2.021215{col 81}{space 3} 6.787308
{txt}{space 23}12  {c |}{col 28}{res}{space 2} 1.709868{col 40}{space 2}  .290355{col 51}{space 1}    3.16{col 60}{space 3}0.002{col 68}{space 4} 1.225798{col 81}{space 3} 2.385099
{txt}{space 23}13  {c |}{col 28}{res}{space 2} 1.544219{col 40}{space 2} .3641007{col 51}{space 1}    1.84{col 60}{space 3}0.065{col 68}{space 4} .9727696{col 81}{space 3} 2.451364
{txt}{space 23}14  {c |}{col 28}{res}{space 2} 2.418647{col 40}{space 2} .6620859{col 51}{space 1}    3.23{col 60}{space 3}0.001{col 68}{space 4} 1.414368{col 81}{space 3} 4.136017
{txt}{space 23}15  {c |}{col 28}{res}{space 2} 1.618903{col 40}{space 2} .4115568{col 51}{space 1}    1.90{col 60}{space 3}0.058{col 68}{space 4} .9836234{col 81}{space 3} 2.664483
{txt}{space 23}16  {c |}{col 28}{res}{space 2} .8490932{col 40}{space 2} .1381262{col 51}{space 1}   -1.01{col 60}{space 3}0.315{col 68}{space 4} .6172857{col 81}{space 3} 1.167951
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 1.615731{col 40}{space 2} .1250727{col 51}{space 1}    6.20{col 60}{space 3}0.000{col 68}{space 4} 1.388283{col 81}{space 3} 1.880442
{txt}{space 23}18  {c |}{col 28}{res}{space 2} 1.938672{col 40}{space 2} .5299753{col 51}{space 1}    2.42{col 60}{space 3}0.015{col 68}{space 4} 1.134517{col 81}{space 3} 3.312817
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .8042553{col 40}{space 2} .1243205{col 51}{space 1}   -1.41{col 60}{space 3}0.159{col 68}{space 4} .5940412{col 81}{space 3} 1.088858
{txt}{space 23}20  {c |}{col 28}{res}{space 2} .3030017{col 40}{space 2} .0883753{col 51}{space 1}   -4.09{col 60}{space 3}0.000{col 68}{space 4} .1710718{col 81}{space 3} .5366754
{txt}{space 23}21  {c |}{col 28}{res}{space 2} .8814764{col 40}{space 2} .0799942{col 51}{space 1}   -1.39{col 60}{space 3}0.164{col 68}{space 4}  .737843{col 81}{space 3}  1.05307
{txt}{space 23}22  {c |}{col 28}{res}{space 2} .5227794{col 40}{space 2} .1752233{col 51}{space 1}   -1.94{col 60}{space 3}0.053{col 68}{space 4} .2710291{col 81}{space 3} 1.008373
{txt}{space 23}23  {c |}{col 28}{res}{space 2}  1.17553{col 40}{space 2} .2936578{col 51}{space 1}    0.65{col 60}{space 3}0.517{col 68}{space 4} .7204371{col 81}{space 3} 1.918101
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .3410356{col 40}{space 2} .1339979{col 51}{space 1}   -2.74{col 60}{space 3}0.006{col 68}{space 4} .1578883{col 81}{space 3} .7366299
{txt}{space 23}25  {c |}{col 28}{res}{space 2} 1.502269{col 40}{space 2} .2167586{col 51}{space 1}    2.82{col 60}{space 3}0.005{col 68}{space 4} 1.132218{col 81}{space 3} 1.993267
{txt}{space 23}26  {c |}{col 28}{res}{space 2}   .80574{col 40}{space 2} .1241772{col 51}{space 1}   -1.40{col 60}{space 3}0.161{col 68}{space 4} .5956778{col 81}{space 3} 1.089879
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} 1.381344{col 40}{space 2} .1321202{col 51}{space 1}    3.38{col 60}{space 3}0.001{col 68}{space 4} 1.145217{col 81}{space 3} 1.666157
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 3.335883{col 40}{space 2} 1.073496{col 51}{space 1}    3.74{col 60}{space 3}0.000{col 68}{space 4} 1.775383{col 81}{space 3} 6.268009
{txt}{space 23}30  {c |}{col 28}{res}{space 2} 1.401228{col 40}{space 2} .4054011{col 51}{space 1}    1.17{col 60}{space 3}0.244{col 68}{space 4} .7947687{col 81}{space 3} 2.470456
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 1.941637{col 40}{space 2} .3765196{col 51}{space 1}    3.42{col 60}{space 3}0.001{col 68}{space 4} 1.327713{col 81}{space 3} 2.839435
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 3.049867{col 40}{space 2}  .774135{col 51}{space 1}    4.39{col 60}{space 3}0.000{col 68}{space 4} 1.854487{col 81}{space 3} 5.015772
{txt}{space 23}52  {c |}{col 28}{res}{space 2} 1.571156{col 40}{space 2} .5056538{col 51}{space 1}    1.40{col 60}{space 3}0.160{col 68}{space 4} .8361271{col 81}{space 3} 2.952339
{txt}{space 23}53  {c |}{col 28}{res}{space 2} 1.501159{col 40}{space 2} .1594852{col 51}{space 1}    3.82{col 60}{space 3}0.000{col 68}{space 4} 1.218973{col 81}{space 3} 1.848671
{txt}{space 23}54  {c |}{col 28}{res}{space 2} 1.599048{col 40}{space 2} .3037601{col 51}{space 1}    2.47{col 60}{space 3}0.013{col 68}{space 4} 1.101957{col 81}{space 3} 2.320376
{txt}{space 23}55  {c |}{col 28}{res}{space 2} 1.062978{col 40}{space 2} .3619615{col 51}{space 1}    0.18{col 60}{space 3}0.858{col 68}{space 4}  .545351{col 81}{space 3} 2.071917
{txt}{space 23}56  {c |}{col 28}{res}{space 2} .9789283{col 40}{space 2}  .357858{col 51}{space 1}   -0.06{col 60}{space 3}0.954{col 68}{space 4} .4781728{col 81}{space 3} 2.004089
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2} 1.157172{col 40}{space 2} .3752958{col 51}{space 1}    0.45{col 60}{space 3}0.653{col 68}{space 4} .6128225{col 81}{space 3} 2.185047
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .3777419{col 40}{space 2} .0908125{col 51}{space 1}   -4.05{col 60}{space 3}0.000{col 68}{space 4} .2358081{col 81}{space 3} .6051062
{txt}{space 23}60  {c |}{col 28}{res}{space 2} .9463974{col 40}{space 2} .1372484{col 51}{space 1}   -0.38{col 60}{space 3}0.704{col 68}{space 4} .7122471{col 81}{space 3} 1.257524
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 20}reagan {c |}{col 28}{res}{space 2} .0624967{col 40}{space 2} .0587982{col 51}{space 1}   -2.95{col 60}{space 3}0.003{col 68}{space 4} .0098862{col 81}{space 3} .3950805
{txt}{space 20}bush41 {c |}{col 28}{res}{space 2} .1549419{col 40}{space 2} .0945989{col 51}{space 1}   -3.05{col 60}{space 3}0.002{col 68}{space 4} .0468244{col 81}{space 3} .5127019
{txt}{space 19}clinton {c |}{col 28}{res}{space 2} .6171753{col 40}{space 2}  .330683{col 51}{space 1}   -0.90{col 60}{space 3}0.368{col 68}{space 4} .2159405{col 81}{space 3} 1.763936
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2} .2147036{col 40}{space 2} .1571096{col 51}{space 1}   -2.10{col 60}{space 3}0.036{col 68}{space 4} .0511648{col 81}{space 3} .9009633
{txt}{space 21}_cons {c |}{col 28}{res}{space 2}  .000333{col 40}{space 2} .0018214{col 51}{space 1}   -1.46{col 60}{space 3}0.143{col 68}{space 4} 7.35e-09{col 81}{space 3} 15.09087
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} .9878179{col 40}{space 2} .0303399{col 51}{space 1}   32.56{col 60}{space 3}0.000{col 68}{space 4} .9283529{col 81}{space 3} 1.047283
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.685368{col 40}{space 2} .0814737{col 68}{space 4} 2.530338{col 81}{space 3} 2.849897
{txt}                       1/p {c |}{col 28}{res}{space 2} .3723884{col 40}{space 2} .0112982{col 68}{space 4} .3508898{col 81}{space 3} .3952041
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store model4a
{txt}
{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 924.6994{col 26}{space 2} 26.60939{col 37}{space 1}   34.75{col 46}{space 3}0.000{col 54}{space 4} 872.5459{col 67}{space 3} 976.8528
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1168.908{col 26}{space 2} 58.94499{col 37}{space 1}   19.83{col 46}{space 3}0.000{col 54}{space 4} 1053.378{col 67}{space 3} 1284.438
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}    10.06{col 38}{space 2}   0.0015
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 244.2082{col 26}{space 2} 77.00348{col 37}{space 5} 93.28416{col 51}{space 3} 395.1323
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix model4azloyal = r(table)
{txt}
{com}. mat list model4azloyal
{res}
{txt}model4azloyal[9,1]
            r2vs1.
              _at
     b {res} 244.20821
{txt}    se {res} 77.003481
{txt}     z {res} 3.1713918
{txt}pvalue {res}  .0015171
{txt}    ll {res} 93.284156
{txt}    ul {res} 395.13225
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. estimates restore model4a
{txt}(results {stata estimates replay model4a:model4a} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 885.9905{col 26}{space 2} 35.34227{col 37}{space 1}   25.07{col 46}{space 3}0.000{col 54}{space 4}  816.721{col 67}{space 3} 955.2601
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1327.694{col 26}{space 2} 116.1924{col 37}{space 1}   11.43{col 46}{space 3}0.000{col 54}{space 4} 1099.961{col 67}{space 3} 1555.427
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     9.14{col 38}{space 2}   0.0025
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 441.7037{col 26}{space 2} 146.1002{col 37}{space 5} 155.3525{col 51}{space 3} 728.0549
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix model4bzloyal = r(table)
{txt}
{com}. mat list model4bzloyal
{res}
{txt}model4bzloyal[9,1]
            r2vs1.
              _at
     b {res}  441.7037
{txt}    se {res} 146.10022
{txt}     z {res} 3.0232925
{txt}pvalue {res}  .0025004
{txt}    ll {res} 155.35253
{txt}    ul {res} 728.05486
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. *****************************************************************************************************************************************************************************************
. 
. 
. 
. 
. 
. **** ALTERNATIVE ESTIMATION OF REPORTED MODEL B1: GOMPERTZ & GENERALIZED GAMMA REGRESSION MODELING  ***
. 
. 
. 
. 
. **** MODEL A1.1: GOMPERTZ MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i. okstartadyr  i.sbagency reagan bush41 clinton bush43, distribution(gompertz) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:
{res}
{txt}{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1012.6928}  
Iteration 1:{space 3}log pseudolikelihood = {res:-947.72416}  
Iteration 2:{space 3}log pseudolikelihood = {res:-902.21936}  
Iteration 3:{space 3}log pseudolikelihood = {res:-901.95389}  
Iteration 4:{space 3}log pseudolikelihood = {res:-901.95384}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-901.95384}  
Iteration 1:{space 3}log pseudolikelihood = {res: -667.3456}  
Iteration 2:{space 3}log pseudolikelihood = {res:-589.95609}  
Iteration 3:{space 3}log pseudolikelihood = {res:-589.39328}  
Iteration 4:{space 3}log pseudolikelihood = {res:-589.39265}  
Iteration 5:{space 3}log pseudolikelihood = {res:-589.39265}  
{res}
{txt}Gompertz PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(22)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-589.39265             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.404162{col 48}{space 2} .1535181{col 59}{space 1}    3.10{col 68}{space 3}0.002{col 76}{space 4} 1.133325{col 89}{space 3} 1.739721
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2}   1.2242{col 48}{space 2}  .229775{col 59}{space 1}    1.08{col 68}{space 3}0.281{col 76}{space 4} .8473969{col 89}{space 3} 1.768552
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6434167{col 48}{space 2} .0870886{col 59}{space 1}   -3.26{col 68}{space 3}0.001{col 76}{space 4} .4934912{col 89}{space 3} .8388906
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.094011{col 48}{space 2} .0663198{col 59}{space 1}    1.48{col 68}{space 3}0.138{col 76}{space 4} .9714518{col 89}{space 3} 1.232033
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .9681768{col 48}{space 2} .0499048{col 59}{space 1}   -0.63{col 68}{space 3}0.530{col 76}{space 4} .8751438{col 89}{space 3}   1.0711
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .5144181{col 48}{space 2} .0653955{col 59}{space 1}   -5.23{col 68}{space 3}0.000{col 76}{space 4} .4009656{col 89}{space 3} .6599718
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .6702588{col 48}{space 2} .1224409{col 59}{space 1}   -2.19{col 68}{space 3}0.029{col 76}{space 4}  .468541{col 89}{space 3}  .958821
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2}  .650137{col 48}{space 2} .1248833{col 59}{space 1}   -2.24{col 68}{space 3}0.025{col 76}{space 4} .4461692{col 89}{space 3} .9473495
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 1.853573{col 48}{space 2} .3066874{col 59}{space 1}    3.73{col 68}{space 3}0.000{col 76}{space 4} 1.340207{col 89}{space 3} 2.563582
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 1.717585{col 48}{space 2} .4043426{col 59}{space 1}    2.30{col 68}{space 3}0.022{col 76}{space 4} 1.082764{col 89}{space 3} 2.724599
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 1.48e-07{col 48}{space 2} 1.38e-06{col 59}{space 1}   -1.68{col 68}{space 3}0.092{col 76}{space 4} 1.68e-15{col 89}{space 3} 13.06709
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 300.1168{col 48}{space 2} 694.1564{col 59}{space 1}    2.47{col 68}{space 3}0.014{col 76}{space 4} 3.224781{col 89}{space 3} 27930.61
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .2037126{col 48}{space 2} .0432395{col 59}{space 1}   -7.50{col 68}{space 3}0.000{col 76}{space 4}  .134383{col 89}{space 3} .3088101
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .9984988{col 48}{space 2} .0041856{col 59}{space 1}   -0.36{col 68}{space 3}0.720{col 76}{space 4} .9903289{col 89}{space 3} 1.006736
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 1.045513{col 48}{space 2} .0960047{col 59}{space 1}    0.48{col 68}{space 3}0.628{col 76}{space 4} .8733085{col 89}{space 3} 1.251675
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 1.476659{col 48}{space 2} .3670375{col 59}{space 1}    1.57{col 68}{space 3}0.117{col 76}{space 4} .9072064{col 89}{space 3} 2.403557
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 3.753281{col 48}{space 2}  .772403{col 59}{space 1}    6.43{col 68}{space 3}0.000{col 76}{space 4} 2.507488{col 89}{space 3} 5.618021
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 3.338654{col 48}{space 2} .9569079{col 59}{space 1}    4.21{col 68}{space 3}0.000{col 76}{space 4} 1.903726{col 89}{space 3} 5.855156
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.514012{col 48}{space 2} .3558082{col 59}{space 1}    1.76{col 68}{space 3}0.078{col 76}{space 4} .9551872{col 89}{space 3} 2.399774
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 3.019238{col 48}{space 2}  .699036{col 59}{space 1}    4.77{col 68}{space 3}0.000{col 76}{space 4} 1.917878{col 89}{space 3} 4.753065
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 4.455354{col 48}{space 2} 1.051195{col 59}{space 1}    6.33{col 68}{space 3}0.000{col 76}{space 4} 2.805756{col 89}{space 3} 7.074805
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 7.116389{col 48}{space 2} 1.883208{col 59}{space 1}    7.42{col 68}{space 3}0.000{col 76}{space 4} 4.236493{col 89}{space 3} 11.95399
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 2.569707{col 48}{space 2} .5239632{col 59}{space 1}    4.63{col 68}{space 3}0.000{col 76}{space 4} 1.723153{col 89}{space 3}  3.83216
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 1.835583{col 48}{space 2}  .386982{col 59}{space 1}    2.88{col 68}{space 3}0.004{col 76}{space 4} 1.214288{col 89}{space 3} 2.774766
{txt}{space 32}4  {c |}{col 36}{res}{space 2} .7618767{col 48}{space 2} .1731349{col 59}{space 1}   -1.20{col 68}{space 3}0.231{col 76}{space 4} .4880349{col 89}{space 3} 1.189374
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.219382{col 48}{space 2} .2739477{col 59}{space 1}    0.88{col 68}{space 3}0.377{col 76}{space 4} .7850691{col 89}{space 3} 1.893964
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.149455{col 48}{space 2} .4566851{col 59}{space 1}    3.60{col 68}{space 3}0.000{col 76}{space 4} 1.417351{col 89}{space 3} 3.259713
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 1.818325{col 48}{space 2} .3920947{col 59}{space 1}    2.77{col 68}{space 3}0.006{col 76}{space 4} 1.191578{col 89}{space 3} 2.774728
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 2.572238{col 48}{space 2} .5161588{col 59}{space 1}    4.71{col 68}{space 3}0.000{col 76}{space 4}  1.73582{col 89}{space 3} 3.811692
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 2.103214{col 48}{space 2}  .423213{col 59}{space 1}    3.69{col 68}{space 3}0.000{col 76}{space 4} 1.417761{col 89}{space 3} 3.120067
{txt}{space 31}11  {c |}{col 36}{res}{space 2} 3.111166{col 48}{space 2} .7779546{col 59}{space 1}    4.54{col 68}{space 3}0.000{col 76}{space 4} 1.905804{col 89}{space 3} 5.078883
{txt}{space 31}12  {c |}{col 36}{res}{space 2}  1.81976{col 48}{space 2} .3075267{col 59}{space 1}    3.54{col 68}{space 3}0.000{col 76}{space 4} 1.306673{col 89}{space 3} 2.534319
{txt}{space 31}13  {c |}{col 36}{res}{space 2} 1.648103{col 48}{space 2} .3006958{col 59}{space 1}    2.74{col 68}{space 3}0.006{col 76}{space 4} 1.152611{col 89}{space 3} 2.356599
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 2.264343{col 48}{space 2} .4874645{col 59}{space 1}    3.80{col 68}{space 3}0.000{col 76}{space 4} 1.484899{col 89}{space 3}  3.45293
{txt}{space 31}15  {c |}{col 36}{res}{space 2}  1.66784{col 48}{space 2} .3057556{col 59}{space 1}    2.79{col 68}{space 3}0.005{col 76}{space 4} 1.164417{col 89}{space 3} 2.388913
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .8487434{col 48}{space 2} .1405867{col 59}{space 1}   -0.99{col 68}{space 3}0.322{col 76}{space 4} .6134548{col 89}{space 3} 1.174276
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 1.483462{col 48}{space 2} .0896824{col 59}{space 1}    6.52{col 68}{space 3}0.000{col 76}{space 4} 1.317702{col 89}{space 3} 1.670074
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 2.061355{col 48}{space 2} .4389954{col 59}{space 1}    3.40{col 68}{space 3}0.001{col 76}{space 4} 1.357929{col 89}{space 3} 3.129166
{txt}{space 31}19  {c |}{col 36}{res}{space 2} .7111683{col 48}{space 2} .1428196{col 59}{space 1}   -1.70{col 68}{space 3}0.090{col 76}{space 4} .4797674{col 89}{space 3} 1.054178
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .3541654{col 48}{space 2} .0755047{col 59}{space 1}   -4.87{col 68}{space 3}0.000{col 76}{space 4} .2332051{col 89}{space 3} .5378662
{txt}{space 31}21  {c |}{col 36}{res}{space 2}  .987753{col 48}{space 2} .1277502{col 59}{space 1}   -0.10{col 68}{space 3}0.924{col 76}{space 4} .7665826{col 89}{space 3} 1.272734
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .4282734{col 48}{space 2} .1614952{col 59}{space 1}   -2.25{col 68}{space 3}0.025{col 76}{space 4} .2045249{col 89}{space 3} .8968006
{txt}{space 31}23  {c |}{col 36}{res}{space 2}  1.35205{col 48}{space 2} .2846703{col 59}{space 1}    1.43{col 68}{space 3}0.152{col 76}{space 4}    .8949{col 89}{space 3} 2.042729
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .1732177{col 48}{space 2} .0900371{col 59}{space 1}   -3.37{col 68}{space 3}0.001{col 76}{space 4} .0625381{col 89}{space 3} .4797773
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.771377{col 48}{space 2} .3107549{col 59}{space 1}    3.26{col 68}{space 3}0.001{col 76}{space 4} 1.255983{col 89}{space 3} 2.498264
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .7615138{col 48}{space 2} .1124484{col 59}{space 1}   -1.85{col 68}{space 3}0.065{col 76}{space 4} .5701455{col 89}{space 3} 1.017115
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} 1.014806{col 48}{space 2} .1082424{col 59}{space 1}    0.14{col 68}{space 3}0.890{col 76}{space 4} .8233628{col 89}{space 3} 1.250762
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 3.875636{col 48}{space 2} 1.077799{col 59}{space 1}    4.87{col 68}{space 3}0.000{col 76}{space 4} 2.247125{col 89}{space 3} 6.684342
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 1.503236{col 48}{space 2} .4382891{col 59}{space 1}    1.40{col 68}{space 3}0.162{col 76}{space 4} .8488827{col 89}{space 3} 2.661992
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 1.856673{col 48}{space 2} .3280436{col 59}{space 1}    3.50{col 68}{space 3}0.000{col 76}{space 4} 1.313234{col 89}{space 3} 2.624997
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 2.780289{col 48}{space 2} .7000549{col 59}{space 1}    4.06{col 68}{space 3}0.000{col 76}{space 4} 1.697322{col 89}{space 3} 4.554239
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.653502{col 48}{space 2} .4442294{col 59}{space 1}    1.87{col 68}{space 3}0.061{col 76}{space 4} .9766096{col 89}{space 3}  2.79955
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.463533{col 48}{space 2} .1376331{col 59}{space 1}    4.05{col 68}{space 3}0.000{col 76}{space 4} 1.217178{col 89}{space 3}  1.75975
{txt}{space 31}54  {c |}{col 36}{res}{space 2} 1.426123{col 48}{space 2} .2496307{col 59}{space 1}    2.03{col 68}{space 3}0.043{col 76}{space 4} 1.011956{col 89}{space 3} 2.009799
{txt}{space 31}55  {c |}{col 36}{res}{space 2} 1.228715{col 48}{space 2} .3889885{col 59}{space 1}    0.65{col 68}{space 3}0.515{col 76}{space 4}  .660658{col 89}{space 3} 2.285209
{txt}{space 31}56  {c |}{col 36}{res}{space 2} 1.110382{col 48}{space 2} .3397304{col 59}{space 1}    0.34{col 68}{space 3}0.732{col 76}{space 4} .6095934{col 89}{space 3} 2.022574
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} .8445832{col 48}{space 2} .2596798{col 59}{space 1}   -0.55{col 68}{space 3}0.583{col 76}{space 4}  .462304{col 89}{space 3} 1.542969
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .0879591{col 48}{space 2} .0269168{col 59}{space 1}   -7.94{col 68}{space 3}0.000{col 76}{space 4} .0482837{col 89}{space 3} .1602365
{txt}{space 31}60  {c |}{col 36}{res}{space 2} .6837448{col 48}{space 2} .0575531{col 59}{space 1}   -4.52{col 68}{space 3}0.000{col 76}{space 4} .5797563{col 89}{space 3} .8063851
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} .0939503{col 48}{space 2} .0916168{col 59}{space 1}   -2.43{col 68}{space 3}0.015{col 76}{space 4} .0138944{col 89}{space 3} .6352686
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2}  .166826{col 48}{space 2} .1099712{col 59}{space 1}   -2.72{col 68}{space 3}0.007{col 76}{space 4} .0458304{col 89}{space 3} .6072579
{txt}{space 27}clinton {c |}{col 36}{res}{space 2}  .393803{col 48}{space 2} .2275928{col 59}{space 1}   -1.61{col 68}{space 3}0.107{col 76}{space 4} .1268643{col 89}{space 3} 1.222415
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2}  .194038{col 48}{space 2} .1626197{col 59}{space 1}   -1.96{col 68}{space 3}0.050{col 76}{space 4} .0375414{col 89}{space 3} 1.002912
{txt}{space 29}_cons {c |}{col 36}{res}{space 2} .2211953{col 48}{space 2} 1.106375{col 59}{space 1}   -0.30{col 68}{space 3}0.763{col 76}{space 4} .0000122{col 89}{space 3} 4002.381
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 28}/gamma {c |}{col 36}{res}{space 2} .0018785{col 48}{space 2} .0001187{col 59}{space 1}   15.82{col 68}{space 3}0.000{col 76}{space 4} .0016458{col 89}{space 3} .0021112
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-901.9538{col 39}-589.3927{col 50}    24{col 58} 1226.785{col 69} 1340.952
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. 
. *** COMPUTE Figure A1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}STANDALONE − NON-STANDALONE Difference{c )-} {c -(}{c -(}2 [M2 & M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQ = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5476842{col 26}{space 2} .1012126{col 37}{space 1}   -3.26{col 46}{space 3}0.001{col 54}{space 4} .3812653{col 67}{space 3} .7867434
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelA11zloyal = r(table)
{txt}
{com}. mat list modelA11zloyal
{res}
{txt}modelA11zloyal[9,1]
               (1)
     b {res}  .54768416
{txt}    se {res}   .1012126
{txt}     z {res} -3.2578633
{txt}pvalue {res}  .00112254
{txt}    ll {res}  .38126527
{txt}    ul {res}  .78674339
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. 
. **** COMPUTE Figure A2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. drop loyalppdiff
{txt}
{com}. generate loyalppdiff = soubinaryagency2nom*zloyalmedian
{txt}
{com}. 
. ** Re-Estimate Model A11  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.sbagency reagan bush41 clinton bush43, distribution(gompertz) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:
{res}
{txt}{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1012.6928}  
Iteration 1:{space 3}log pseudolikelihood = {res:-947.72416}  
Iteration 2:{space 3}log pseudolikelihood = {res:-902.21936}  
Iteration 3:{space 3}log pseudolikelihood = {res:-901.95389}  
Iteration 4:{space 3}log pseudolikelihood = {res:-901.95384}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-901.95384}  
Iteration 1:{space 3}log pseudolikelihood = {res: -667.3456}  
Iteration 2:{space 3}log pseudolikelihood = {res:-589.95609}  
Iteration 3:{space 3}log pseudolikelihood = {res:-589.39328}  
Iteration 4:{space 3}log pseudolikelihood = {res:-589.39265}  
Iteration 5:{space 3}log pseudolikelihood = {res:-589.39265}  
{res}
{txt}Gompertz PH regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(22)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-589.39265             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2} 1.404162{col 40}{space 2} .1535181{col 51}{space 1}    3.10{col 60}{space 3}0.002{col 68}{space 4} 1.133325{col 81}{space 3} 1.739721
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2}   1.2242{col 40}{space 2}  .229775{col 51}{space 1}    1.08{col 60}{space 3}0.281{col 68}{space 4} .8473969{col 81}{space 3} 1.768552
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6434167{col 40}{space 2} .0870886{col 51}{space 1}   -3.26{col 60}{space 3}0.001{col 68}{space 4} .4934912{col 81}{space 3} .8388906
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.094011{col 40}{space 2} .0663198{col 51}{space 1}    1.48{col 60}{space 3}0.138{col 68}{space 4} .9714518{col 81}{space 3} 1.232033
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .9681768{col 40}{space 2} .0499048{col 51}{space 1}   -0.63{col 60}{space 3}0.530{col 68}{space 4} .8751438{col 81}{space 3}   1.0711
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5144181{col 40}{space 2} .0653955{col 51}{space 1}   -5.23{col 60}{space 3}0.000{col 68}{space 4} .4009656{col 81}{space 3} .6599718
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} .6702588{col 40}{space 2} .1224409{col 51}{space 1}   -2.19{col 60}{space 3}0.029{col 68}{space 4}  .468541{col 81}{space 3}  .958821
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2}  .650137{col 40}{space 2} .1248833{col 51}{space 1}   -2.24{col 60}{space 3}0.025{col 68}{space 4} .4461692{col 81}{space 3} .9473495
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 1.853573{col 40}{space 2} .3066874{col 51}{space 1}    3.73{col 60}{space 3}0.000{col 68}{space 4} 1.340207{col 81}{space 3} 2.563582
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} 1.717585{col 40}{space 2} .4043426{col 51}{space 1}    2.30{col 60}{space 3}0.022{col 68}{space 4} 1.082764{col 81}{space 3} 2.724599
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 1.48e-07{col 40}{space 2} 1.38e-06{col 51}{space 1}   -1.68{col 60}{space 3}0.092{col 68}{space 4} 1.68e-15{col 81}{space 3} 13.06709
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 300.1168{col 40}{space 2} 694.1564{col 51}{space 1}    2.47{col 60}{space 3}0.014{col 68}{space 4} 3.224781{col 81}{space 3} 27930.61
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .2037126{col 40}{space 2} .0432395{col 51}{space 1}   -7.50{col 60}{space 3}0.000{col 68}{space 4}  .134383{col 81}{space 3} .3088101
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9984988{col 40}{space 2} .0041856{col 51}{space 1}   -0.36{col 60}{space 3}0.720{col 68}{space 4} .9903289{col 81}{space 3} 1.006736
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} 1.045513{col 40}{space 2} .0960047{col 51}{space 1}    0.48{col 60}{space 3}0.628{col 68}{space 4} .8733085{col 81}{space 3} 1.251675
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 1.476659{col 40}{space 2} .3670375{col 51}{space 1}    1.57{col 60}{space 3}0.117{col 68}{space 4} .9072064{col 81}{space 3} 2.403557
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 3.753281{col 40}{space 2}  .772403{col 51}{space 1}    6.43{col 60}{space 3}0.000{col 68}{space 4} 2.507488{col 81}{space 3} 5.618021
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 3.338654{col 40}{space 2} .9569079{col 51}{space 1}    4.21{col 60}{space 3}0.000{col 68}{space 4} 1.903726{col 81}{space 3} 5.855156
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.514012{col 40}{space 2} .3558082{col 51}{space 1}    1.76{col 60}{space 3}0.078{col 68}{space 4} .9551872{col 81}{space 3} 2.399774
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 3.019238{col 40}{space 2}  .699036{col 51}{space 1}    4.77{col 60}{space 3}0.000{col 68}{space 4} 1.917878{col 81}{space 3} 4.753065
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 4.455354{col 40}{space 2} 1.051195{col 51}{space 1}    6.33{col 60}{space 3}0.000{col 68}{space 4} 2.805756{col 81}{space 3} 7.074805
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 7.116389{col 40}{space 2} 1.883208{col 51}{space 1}    7.42{col 60}{space 3}0.000{col 68}{space 4} 4.236493{col 81}{space 3} 11.95399
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 2.569707{col 40}{space 2} .5239632{col 51}{space 1}    4.63{col 60}{space 3}0.000{col 68}{space 4} 1.723153{col 81}{space 3}  3.83216
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 1.835583{col 40}{space 2}  .386982{col 51}{space 1}    2.88{col 60}{space 3}0.004{col 68}{space 4} 1.214288{col 81}{space 3} 2.774766
{txt}{space 24}4  {c |}{col 28}{res}{space 2} .7618767{col 40}{space 2} .1731349{col 51}{space 1}   -1.20{col 60}{space 3}0.231{col 68}{space 4} .4880349{col 81}{space 3} 1.189374
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.219382{col 40}{space 2} .2739477{col 51}{space 1}    0.88{col 60}{space 3}0.377{col 68}{space 4} .7850691{col 81}{space 3} 1.893964
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.149455{col 40}{space 2} .4566851{col 51}{space 1}    3.60{col 60}{space 3}0.000{col 68}{space 4} 1.417351{col 81}{space 3} 3.259713
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 1.818325{col 40}{space 2} .3920947{col 51}{space 1}    2.77{col 60}{space 3}0.006{col 68}{space 4} 1.191578{col 81}{space 3} 2.774728
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 2.572238{col 40}{space 2} .5161588{col 51}{space 1}    4.71{col 60}{space 3}0.000{col 68}{space 4}  1.73582{col 81}{space 3} 3.811692
{txt}{space 24}9  {c |}{col 28}{res}{space 2} 2.103214{col 40}{space 2}  .423213{col 51}{space 1}    3.69{col 60}{space 3}0.000{col 68}{space 4} 1.417761{col 81}{space 3} 3.120067
{txt}{space 23}11  {c |}{col 28}{res}{space 2} 3.111166{col 40}{space 2} .7779546{col 51}{space 1}    4.54{col 60}{space 3}0.000{col 68}{space 4} 1.905804{col 81}{space 3} 5.078883
{txt}{space 23}12  {c |}{col 28}{res}{space 2}  1.81976{col 40}{space 2} .3075267{col 51}{space 1}    3.54{col 60}{space 3}0.000{col 68}{space 4} 1.306673{col 81}{space 3} 2.534319
{txt}{space 23}13  {c |}{col 28}{res}{space 2} 1.648103{col 40}{space 2} .3006958{col 51}{space 1}    2.74{col 60}{space 3}0.006{col 68}{space 4} 1.152611{col 81}{space 3} 2.356599
{txt}{space 23}14  {c |}{col 28}{res}{space 2} 2.264343{col 40}{space 2} .4874645{col 51}{space 1}    3.80{col 60}{space 3}0.000{col 68}{space 4} 1.484899{col 81}{space 3}  3.45293
{txt}{space 23}15  {c |}{col 28}{res}{space 2}  1.66784{col 40}{space 2} .3057556{col 51}{space 1}    2.79{col 60}{space 3}0.005{col 68}{space 4} 1.164417{col 81}{space 3} 2.388913
{txt}{space 23}16  {c |}{col 28}{res}{space 2} .8487434{col 40}{space 2} .1405867{col 51}{space 1}   -0.99{col 60}{space 3}0.322{col 68}{space 4} .6134548{col 81}{space 3} 1.174276
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 1.483462{col 40}{space 2} .0896824{col 51}{space 1}    6.52{col 60}{space 3}0.000{col 68}{space 4} 1.317702{col 81}{space 3} 1.670074
{txt}{space 23}18  {c |}{col 28}{res}{space 2} 2.061355{col 40}{space 2} .4389954{col 51}{space 1}    3.40{col 60}{space 3}0.001{col 68}{space 4} 1.357929{col 81}{space 3} 3.129166
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .7111683{col 40}{space 2} .1428196{col 51}{space 1}   -1.70{col 60}{space 3}0.090{col 68}{space 4} .4797674{col 81}{space 3} 1.054178
{txt}{space 23}20  {c |}{col 28}{res}{space 2} .3541654{col 40}{space 2} .0755047{col 51}{space 1}   -4.87{col 60}{space 3}0.000{col 68}{space 4} .2332051{col 81}{space 3} .5378662
{txt}{space 23}21  {c |}{col 28}{res}{space 2}  .987753{col 40}{space 2} .1277502{col 51}{space 1}   -0.10{col 60}{space 3}0.924{col 68}{space 4} .7665826{col 81}{space 3} 1.272734
{txt}{space 23}22  {c |}{col 28}{res}{space 2} .4282734{col 40}{space 2} .1614952{col 51}{space 1}   -2.25{col 60}{space 3}0.025{col 68}{space 4} .2045249{col 81}{space 3} .8968006
{txt}{space 23}23  {c |}{col 28}{res}{space 2}  1.35205{col 40}{space 2} .2846703{col 51}{space 1}    1.43{col 60}{space 3}0.152{col 68}{space 4}    .8949{col 81}{space 3} 2.042729
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .1732177{col 40}{space 2} .0900371{col 51}{space 1}   -3.37{col 60}{space 3}0.001{col 68}{space 4} .0625381{col 81}{space 3} .4797773
{txt}{space 23}25  {c |}{col 28}{res}{space 2} 1.771377{col 40}{space 2} .3107549{col 51}{space 1}    3.26{col 60}{space 3}0.001{col 68}{space 4} 1.255983{col 81}{space 3} 2.498264
{txt}{space 23}26  {c |}{col 28}{res}{space 2} .7615138{col 40}{space 2} .1124484{col 51}{space 1}   -1.85{col 60}{space 3}0.065{col 68}{space 4} .5701455{col 81}{space 3} 1.017115
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} 1.014806{col 40}{space 2} .1082424{col 51}{space 1}    0.14{col 60}{space 3}0.890{col 68}{space 4} .8233628{col 81}{space 3} 1.250762
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 3.875636{col 40}{space 2} 1.077799{col 51}{space 1}    4.87{col 60}{space 3}0.000{col 68}{space 4} 2.247125{col 81}{space 3} 6.684342
{txt}{space 23}30  {c |}{col 28}{res}{space 2} 1.503236{col 40}{space 2} .4382891{col 51}{space 1}    1.40{col 60}{space 3}0.162{col 68}{space 4} .8488827{col 81}{space 3} 2.661992
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 1.856673{col 40}{space 2} .3280436{col 51}{space 1}    3.50{col 60}{space 3}0.000{col 68}{space 4} 1.313234{col 81}{space 3} 2.624997
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 2.780289{col 40}{space 2} .7000549{col 51}{space 1}    4.06{col 60}{space 3}0.000{col 68}{space 4} 1.697322{col 81}{space 3} 4.554239
{txt}{space 23}52  {c |}{col 28}{res}{space 2} 1.653502{col 40}{space 2} .4442294{col 51}{space 1}    1.87{col 60}{space 3}0.061{col 68}{space 4} .9766096{col 81}{space 3}  2.79955
{txt}{space 23}53  {c |}{col 28}{res}{space 2} 1.463533{col 40}{space 2} .1376331{col 51}{space 1}    4.05{col 60}{space 3}0.000{col 68}{space 4} 1.217178{col 81}{space 3}  1.75975
{txt}{space 23}54  {c |}{col 28}{res}{space 2} 1.426123{col 40}{space 2} .2496307{col 51}{space 1}    2.03{col 60}{space 3}0.043{col 68}{space 4} 1.011956{col 81}{space 3} 2.009799
{txt}{space 23}55  {c |}{col 28}{res}{space 2} 1.228715{col 40}{space 2} .3889885{col 51}{space 1}    0.65{col 60}{space 3}0.515{col 68}{space 4}  .660658{col 81}{space 3} 2.285209
{txt}{space 23}56  {c |}{col 28}{res}{space 2} 1.110382{col 40}{space 2} .3397304{col 51}{space 1}    0.34{col 60}{space 3}0.732{col 68}{space 4} .6095934{col 81}{space 3} 2.022574
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2} .8445832{col 40}{space 2} .2596798{col 51}{space 1}   -0.55{col 60}{space 3}0.583{col 68}{space 4}  .462304{col 81}{space 3} 1.542969
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .0879591{col 40}{space 2} .0269168{col 51}{space 1}   -7.94{col 60}{space 3}0.000{col 68}{space 4} .0482837{col 81}{space 3} .1602365
{txt}{space 23}60  {c |}{col 28}{res}{space 2} .6837448{col 40}{space 2} .0575531{col 51}{space 1}   -4.52{col 60}{space 3}0.000{col 68}{space 4} .5797563{col 81}{space 3} .8063851
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 20}reagan {c |}{col 28}{res}{space 2} .0939503{col 40}{space 2} .0916168{col 51}{space 1}   -2.43{col 60}{space 3}0.015{col 68}{space 4} .0138944{col 81}{space 3} .6352686
{txt}{space 20}bush41 {c |}{col 28}{res}{space 2}  .166826{col 40}{space 2} .1099712{col 51}{space 1}   -2.72{col 60}{space 3}0.007{col 68}{space 4} .0458304{col 81}{space 3} .6072579
{txt}{space 19}clinton {c |}{col 28}{res}{space 2}  .393803{col 40}{space 2} .2275928{col 51}{space 1}   -1.61{col 60}{space 3}0.107{col 68}{space 4} .1268643{col 81}{space 3} 1.222415
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2}  .194038{col 40}{space 2} .1626197{col 51}{space 1}   -1.96{col 60}{space 3}0.050{col 68}{space 4} .0375414{col 81}{space 3} 1.002912
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .2211953{col 40}{space 2} 1.106375{col 51}{space 1}   -0.30{col 60}{space 3}0.763{col 68}{space 4} .0000122{col 81}{space 3} 4002.381
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 20}/gamma {c |}{col 28}{res}{space 2} .0018785{col 40}{space 2} .0001187{col 51}{space 1}   15.82{col 60}{space 3}0.000{col 68}{space 4} .0016458{col 81}{space 3} .0021112
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store modela11
{txt}
{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 938.4124{col 26}{space 2} 37.74019{col 37}{space 1}   24.87{col 46}{space 3}0.000{col 54}{space 4}  864.443{col 67}{space 3} 1012.382
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1194.938{col 26}{space 2} 51.94518{col 37}{space 1}   23.00{col 46}{space 3}0.000{col 54}{space 4} 1093.128{col 67}{space 3} 1296.749
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. 
. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     9.33{col 38}{space 2}   0.0023
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 256.5259{col 26}{space 2} 84.00455{col 37}{space 5} 91.88003{col 51}{space 3} 421.1718
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelA11azloyal = r(table)
{txt}
{com}. mat list modelA11azloyal
{res}
{txt}modelA11azloyal[9,1]
            r2vs1.
              _at
     b {res} 256.52593
{txt}    se {res} 84.004552
{txt}     z {res} 3.0537146
{txt}pvalue {res} .00226027
{txt}    ll {res} 91.880033
{txt}    ul {res} 421.17182
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. estimates restore modela11
{txt}(results {stata estimates replay modela11:modela11} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 894.5921{col 26}{space 2} 50.09984{col 37}{space 1}   17.86{col 46}{space 3}0.000{col 54}{space 4} 796.3982{col 67}{space 3}  992.786
{txt}{space 10}2  {c |}{col 14}{res}{space 2}  1344.27{col 26}{space 2} 102.6625{col 37}{space 1}   13.09{col 46}{space 3}0.000{col 54}{space 4} 1143.055{col 67}{space 3} 1545.485
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     9.07{col 38}{space 2}   0.0026
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 449.6779{col 26}{space 2} 149.2853{col 37}{space 5}  157.084{col 51}{space 3} 742.2717
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelA11bzloyal = r(table)
{txt}
{com}. mat list modelA11bzloyal
{res}
{txt}modelA11bzloyal[9,1]
            r2vs1.
              _at
     b {res} 449.67787
{txt}    se {res} 149.28532
{txt}     z {res} 3.0122043
{txt}pvalue {res} .00259358
{txt}    ll {res} 157.08402
{txt}    ul {res} 742.27171
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. 
. 
. 
. 
. **** MODEL A1.2: GENERALIZED GAMMA MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. ** First, Estimate Weibull Model Analog in AFT metric for Comparison Purposes to Generalized Gamma Model **
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  okstartunemp  i. okstartadyr  i.sbagency reagan bush41 clinton bush43,   time distribution(weibull) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: okstartunemployment omitted because of collinearity
note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res}-1012.6928
{txt}Iteration 1:   log pseudolikelihood = {res}-835.21164
{txt}Iteration 2:   log pseudolikelihood = {res}-830.85586
{txt}Iteration 3:   log pseudolikelihood = {res}-830.85509
{txt}Iteration 4:   log pseudolikelihood = {res}-830.85509

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-830.85509}  
Iteration 1:{space 3}log pseudolikelihood = {res:-604.21225}  
Iteration 2:{space 3}log pseudolikelihood = {res:-499.15183}  
Iteration 3:{space 3}log pseudolikelihood = {res:-497.85317}  
Iteration 4:{space 3}log pseudolikelihood = {res:-497.85043}  
Iteration 5:{space 3}log pseudolikelihood = {res:-497.85043}  
{res}
{txt}Weibull AFT regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
                                                {txt}Wald chi2({res}22{txt})    =  {res}  12549.64
{txt}Log pseudolikelihood =   {res}-497.85043             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2}-.1185949{col 48}{space 2} .0432789{col 59}{space 1}   -2.74{col 68}{space 3}0.006{col 76}{space 4}  -.20342{col 89}{space 3}-.0337698
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2}-.0532205{col 48}{space 2} .0665893{col 59}{space 1}   -0.80{col 68}{space 3}0.424{col 76}{space 4} -.183733{col 89}{space 3} .0772921
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .1716489{col 48}{space 2} .0517242{col 59}{space 1}    3.32{col 68}{space 3}0.001{col 76}{space 4} .0702712{col 89}{space 3} .2730266
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2}-.0151679{col 48}{space 2}  .029226{col 59}{space 1}   -0.52{col 68}{space 3}0.604{col 76}{space 4}-.0724498{col 89}{space 3} .0421141
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .0057637{col 48}{space 2}  .024963{col 59}{space 1}    0.23{col 68}{space 3}0.817{col 76}{space 4}-.0431629{col 89}{space 3} .0546903
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2}  .229811{col 48}{space 2} .0396784{col 59}{space 1}    5.79{col 68}{space 3}0.000{col 76}{space 4} .1520429{col 89}{space 3} .3075791
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .1136856{col 48}{space 2} .0930746{col 59}{space 1}    1.22{col 68}{space 3}0.222{col 76}{space 4}-.0687373{col 89}{space 3} .2961084
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2}  .136281{col 48}{space 2} .0942339{col 59}{space 1}    1.45{col 68}{space 3}0.148{col 76}{space 4} -.048414{col 89}{space 3} .3209759
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2}-.1943484{col 48}{space 2} .0661084{col 59}{space 1}   -2.94{col 68}{space 3}0.003{col 76}{space 4}-.3239184{col 89}{space 3}-.0647784
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2}-.2069613{col 48}{space 2} .1020735{col 59}{space 1}   -2.03{col 68}{space 3}0.043{col 76}{space 4}-.4070216{col 89}{space 3}-.0069009
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 8.668398{col 48}{space 2} 3.839502{col 59}{space 1}    2.26{col 68}{space 3}0.024{col 76}{space 4} 1.143112{col 89}{space 3} 16.19368
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2}-2.530557{col 48}{space 2} .8316448{col 59}{space 1}   -3.04{col 68}{space 3}0.002{col 76}{space 4}-4.160551{col 89}{space 3} -.900563
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .6496209{col 48}{space 2} .0680156{col 59}{space 1}    9.55{col 68}{space 3}0.000{col 76}{space 4} .5163127{col 89}{space 3} .7829291
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .0036544{col 48}{space 2} .0016761{col 59}{space 1}    2.18{col 68}{space 3}0.029{col 76}{space 4} .0003694{col 89}{space 3} .0069395
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2}-.0455197{col 48}{space 2}   .03239{col 59}{space 1}   -1.41{col 68}{space 3}0.160{col 76}{space 4} -.109003{col 89}{space 3} .0179635
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2}        0{col 48}{txt}  (omitted)
{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2}-.1872278{col 48}{space 2} .0821112{col 59}{space 1}   -2.28{col 68}{space 3}0.023{col 76}{space 4}-.3481628{col 89}{space 3}-.0262928
{txt}{space 32}3  {c |}{col 36}{res}{space 2}-.5503595{col 48}{space 2} .0770623{col 59}{space 1}   -7.14{col 68}{space 3}0.000{col 76}{space 4}-.7013988{col 89}{space 3}-.3993202
{txt}{space 32}4  {c |}{col 36}{res}{space 2}-.5108656{col 48}{space 2} .1124616{col 59}{space 1}   -4.54{col 68}{space 3}0.000{col 76}{space 4}-.7312862{col 89}{space 3}-.2904449
{txt}{space 32}5  {c |}{col 36}{res}{space 2}-.1579824{col 48}{space 2} .0916759{col 59}{space 1}   -1.72{col 68}{space 3}0.085{col 76}{space 4}-.3376639{col 89}{space 3}  .021699
{txt}{space 32}6  {c |}{col 36}{res}{space 2}-.4612732{col 48}{space 2} .0899822{col 59}{space 1}   -5.13{col 68}{space 3}0.000{col 76}{space 4}-.6376351{col 89}{space 3}-.2849114
{txt}{space 32}7  {c |}{col 36}{res}{space 2}-.6851351{col 48}{space 2} .1008763{col 59}{space 1}   -6.79{col 68}{space 3}0.000{col 76}{space 4} -.882849{col 89}{space 3}-.4874213
{txt}{space 32}8  {c |}{col 36}{res}{space 2}-.8582564{col 48}{space 2} .1336136{col 59}{space 1}   -6.42{col 68}{space 3}0.000{col 76}{space 4}-1.120134{col 89}{space 3}-.5963785
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2}-.3876706{col 48}{space 2} .0992177{col 59}{space 1}   -3.91{col 68}{space 3}0.000{col 76}{space 4}-.5821338{col 89}{space 3}-.1932074
{txt}{space 32}3  {c |}{col 36}{res}{space 2}-.2206625{col 48}{space 2} .0932489{col 59}{space 1}   -2.37{col 68}{space 3}0.018{col 76}{space 4} -.403427{col 89}{space 3}-.0378981
{txt}{space 32}4  {c |}{col 36}{res}{space 2}-.0785186{col 48}{space 2} .0825786{col 59}{space 1}   -0.95{col 68}{space 3}0.342{col 76}{space 4}-.2403697{col 89}{space 3} .0833325
{txt}{space 32}5  {c |}{col 36}{res}{space 2}-.0152615{col 48}{space 2} .0988452{col 59}{space 1}   -0.15{col 68}{space 3}0.877{col 76}{space 4}-.2089946{col 89}{space 3} .1784716
{txt}{space 32}6  {c |}{col 36}{res}{space 2}-.3427019{col 48}{space 2} .0859459{col 59}{space 1}   -3.99{col 68}{space 3}0.000{col 76}{space 4}-.5111527{col 89}{space 3} -.174251
{txt}{space 32}7  {c |}{col 36}{res}{space 2}-.2161157{col 48}{space 2} .1062504{col 59}{space 1}   -2.03{col 68}{space 3}0.042{col 76}{space 4}-.4243626{col 89}{space 3}-.0078688
{txt}{space 32}8  {c |}{col 36}{res}{space 2}-.3218689{col 48}{space 2} .0983309{col 59}{space 1}   -3.27{col 68}{space 3}0.001{col 76}{space 4}-.5145939{col 89}{space 3}-.1291439
{txt}{space 32}9  {c |}{col 36}{res}{space 2}-.2978758{col 48}{space 2} .0923698{col 59}{space 1}   -3.22{col 68}{space 3}0.001{col 76}{space 4}-.4789172{col 89}{space 3}-.1168344
{txt}{space 31}11  {c |}{col 36}{res}{space 2}-.4875966{col 48}{space 2} .1133673{col 59}{space 1}   -4.30{col 68}{space 3}0.000{col 76}{space 4}-.7097924{col 89}{space 3}-.2654008
{txt}{space 31}12  {c |}{col 36}{res}{space 2}-.1997552{col 48}{space 2} .0633026{col 59}{space 1}   -3.16{col 68}{space 3}0.002{col 76}{space 4}-.3238261{col 89}{space 3}-.0756843
{txt}{space 31}13  {c |}{col 36}{res}{space 2}-.1618096{col 48}{space 2} .0875067{col 59}{space 1}   -1.85{col 68}{space 3}0.064{col 76}{space 4}-.3333195{col 89}{space 3} .0097003
{txt}{space 31}14  {c |}{col 36}{res}{space 2}-.3288964{col 48}{space 2} .1016159{col 59}{space 1}   -3.24{col 68}{space 3}0.001{col 76}{space 4}  -.52806{col 89}{space 3}-.1297329
{txt}{space 31}15  {c |}{col 36}{res}{space 2}-.1793977{col 48}{space 2} .0943876{col 59}{space 1}   -1.90{col 68}{space 3}0.057{col 76}{space 4} -.364394{col 89}{space 3} .0055986
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .0609176{col 48}{space 2} .0601137{col 59}{space 1}    1.01{col 68}{space 3}0.311{col 76}{space 4} -.056903{col 89}{space 3} .1787383
{txt}{space 31}17  {c |}{col 36}{res}{space 2}-.1786673{col 48}{space 2} .0288401{col 59}{space 1}   -6.20{col 68}{space 3}0.000{col 76}{space 4}-.2351928{col 89}{space 3}-.1221417
{txt}{space 31}18  {c |}{col 36}{res}{space 2}-.2465222{col 48}{space 2} .1014758{col 59}{space 1}   -2.43{col 68}{space 3}0.015{col 76}{space 4}-.4454112{col 89}{space 3}-.0476332
{txt}{space 31}19  {c |}{col 36}{res}{space 2} .0811205{col 48}{space 2} .0565489{col 59}{space 1}    1.43{col 68}{space 3}0.151{col 76}{space 4}-.0297133{col 89}{space 3} .1919543
{txt}{space 31}20  {c |}{col 36}{res}{space 2}  .444638{col 48}{space 2} .1029169{col 59}{space 1}    4.32{col 68}{space 3}0.000{col 76}{space 4} .2429247{col 89}{space 3} .6463514
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .0469794{col 48}{space 2} .0335096{col 59}{space 1}    1.40{col 68}{space 3}0.161{col 76}{space 4}-.0186981{col 89}{space 3} .1126569
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .2415295{col 48}{space 2} .1223855{col 59}{space 1}    1.97{col 68}{space 3}0.048{col 76}{space 4} .0016583{col 89}{space 3} .4814006
{txt}{space 31}23  {c |}{col 36}{res}{space 2}-.0602223{col 48}{space 2}  .093412{col 59}{space 1}   -0.64{col 68}{space 3}0.519{col 76}{space 4}-.2433065{col 89}{space 3} .1228618
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .4006037{col 48}{space 2} .1433169{col 59}{space 1}    2.80{col 68}{space 3}0.005{col 76}{space 4} .1197078{col 89}{space 3} .6814996
{txt}{space 31}25  {c |}{col 36}{res}{space 2}-.1515534{col 48}{space 2} .0534723{col 59}{space 1}   -2.83{col 68}{space 3}0.005{col 76}{space 4}-.2563572{col 89}{space 3}-.0467497
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .0804337{col 48}{space 2} .0574769{col 59}{space 1}    1.40{col 68}{space 3}0.162{col 76}{space 4} -.032219{col 89}{space 3} .1930865
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        0{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2}-.1203027{col 48}{space 2} .0354368{col 59}{space 1}   -3.39{col 68}{space 3}0.001{col 76}{space 4}-.1897576{col 89}{space 3}-.0508477
{txt}{space 31}29  {c |}{col 36}{res}{space 2}-.4486302{col 48}{space 2}  .119798{col 59}{space 1}   -3.74{col 68}{space 3}0.000{col 76}{space 4}  -.68343{col 89}{space 3}-.2138304
{txt}{space 31}30  {c |}{col 36}{res}{space 2} -.125625{col 48}{space 2} .1077439{col 59}{space 1}   -1.17{col 68}{space 3}0.244{col 76}{space 4}-.3367992{col 89}{space 3} .0855492
{txt}{space 31}50  {c |}{col 36}{res}{space 2}-.2470913{col 48}{space 2} .0716073{col 59}{space 1}   -3.45{col 68}{space 3}0.001{col 76}{space 4}-.3874391{col 89}{space 3}-.1067436
{txt}{space 31}51  {c |}{col 36}{res}{space 2}-.4152495{col 48}{space 2} .0915498{col 59}{space 1}   -4.54{col 68}{space 3}0.000{col 76}{space 4}-.5946838{col 89}{space 3}-.2358151
{txt}{space 31}52  {c |}{col 36}{res}{space 2}-.1682494{col 48}{space 2} .1185762{col 59}{space 1}   -1.42{col 68}{space 3}0.156{col 76}{space 4}-.4006545{col 89}{space 3} .0641558
{txt}{space 31}53  {c |}{col 36}{res}{space 2}-.1512782{col 48}{space 2} .0395075{col 59}{space 1}   -3.83{col 68}{space 3}0.000{col 76}{space 4}-.2287114{col 89}{space 3} -.073845
{txt}{space 31}54  {c |}{col 36}{res}{space 2}-.1748023{col 48}{space 2} .0700812{col 59}{space 1}   -2.49{col 68}{space 3}0.013{col 76}{space 4}-.3121589{col 89}{space 3}-.0374458
{txt}{space 31}55  {c |}{col 36}{res}{space 2}-.0227434{col 48}{space 2} .1267004{col 59}{space 1}   -0.18{col 68}{space 3}0.858{col 76}{space 4}-.2710716{col 89}{space 3} .2255848
{txt}{space 31}56  {c |}{col 36}{res}{space 2} .0079307{col 48}{space 2} .1361683{col 59}{space 1}    0.06{col 68}{space 3}0.954{col 76}{space 4}-.2589542{col 89}{space 3} .2748156
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        0{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2}-.0543608{col 48}{space 2}  .120505{col 59}{space 1}   -0.45{col 68}{space 3}0.652{col 76}{space 4}-.2905463{col 89}{space 3} .1818247
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .3625365{col 48}{space 2} .0880741{col 59}{space 1}    4.12{col 68}{space 3}0.000{col 76}{space 4} .1899144{col 89}{space 3} .5351586
{txt}{space 31}60  {c |}{col 36}{res}{space 2} .0205159{col 48}{space 2} .0541612{col 59}{space 1}    0.38{col 68}{space 3}0.705{col 76}{space 4}-.0856382{col 89}{space 3} .1266699
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        0{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} 1.032499{col 48}{space 2}   .35183{col 59}{space 1}    2.93{col 68}{space 3}0.003{col 76}{space 4} .3429253{col 89}{space 3} 1.722074
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2} .6943946{col 48}{space 2} .2291724{col 59}{space 1}    3.03{col 68}{space 3}0.002{col 76}{space 4} .2452249{col 89}{space 3} 1.143564
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} .1797154{col 48}{space 2} .2008615{col 59}{space 1}    0.89{col 68}{space 3}0.371{col 76}{space 4}-.2139659{col 89}{space 3} .5733967
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2} .5729183{col 48}{space 2} .2754851{col 59}{space 1}    2.08{col 68}{space 3}0.038{col 76}{space 4} .0329774{col 89}{space 3} 1.112859
{txt}{space 29}_cons {c |}{col 36}{res}{space 2} 2.981898{col 48}{space 2} 2.054623{col 59}{space 1}    1.45{col 68}{space 3}0.147{col 76}{space 4} -1.04509{col 89}{space 3} 7.008885
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 29}/ln_p {c |}{col 36}{res}{space 2} .9878179{col 48}{space 2} .0303399{col 59}{space 1}   32.56{col 68}{space 3}0.000{col 76}{space 4} .9283529{col 89}{space 3} 1.047283
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                                 p {c |}{col 36}{res}{space 2} 2.685368{col 48}{space 2} .0814737{col 76}{space 4} 2.530338{col 89}{space 3} 2.849897
{txt}                               1/p {c |}{col 36}{res}{space 2} .3723884{col 48}{space 2} .0112982{col 76}{space 4} .3508898{col 89}{space 3} .3952041
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. *
. ** Now, Estimate the Generalized Gamma Model of Interest **
. 
. streg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  okstartunemp  i. okstartadyr  i.sbagency reagan bush41 clinton bush43,  distribution(ggamma) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: okstartunemployment omitted because of collinearity
note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1012.6928}  
Iteration 1:{space 3}log pseudolikelihood = {res:-823.39319}  
Iteration 2:{space 3}log pseudolikelihood = {res:-820.15856}  
Iteration 3:{space 3}log pseudolikelihood = {res:-820.14466}  
Iteration 4:{space 3}log pseudolikelihood = {res:-820.14466}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-820.14466}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-610.96571}  
Iteration 2:{space 3}log pseudolikelihood = {res:-502.97484}  
Iteration 3:{space 3}log pseudolikelihood = {res:-494.51433}  
Iteration 4:{space 3}log pseudolikelihood = {res:-494.42565}  
Iteration 5:{space 3}log pseudolikelihood = {res:-494.42559}  
Iteration 6:{space 3}log pseudolikelihood = {res:-494.42559}  
{res}
{txt}Generalized gamma AFT regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(22)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-494.42559             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2}-.1217118{col 48}{space 2} .0417528{col 59}{space 1}   -2.92{col 68}{space 3}0.004{col 76}{space 4}-.2035458{col 89}{space 3}-.0398777
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2}-.0649184{col 48}{space 2} .0632468{col 59}{space 1}   -1.03{col 68}{space 3}0.305{col 76}{space 4}-.1888798{col 89}{space 3}  .059043
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .1678771{col 48}{space 2} .0508451{col 59}{space 1}    3.30{col 68}{space 3}0.001{col 76}{space 4} .0682225{col 89}{space 3} .2675317
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2}-.0108729{col 48}{space 2} .0266452{col 59}{space 1}   -0.41{col 68}{space 3}0.683{col 76}{space 4}-.0630966{col 89}{space 3} .0413507
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .0054568{col 48}{space 2} .0242518{col 59}{space 1}    0.23{col 68}{space 3}0.822{col 76}{space 4}-.0420758{col 89}{space 3} .0529894
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .2418702{col 48}{space 2} .0394788{col 59}{space 1}    6.13{col 68}{space 3}0.000{col 76}{space 4} .1644932{col 89}{space 3} .3192472
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .1616202{col 48}{space 2}  .082035{col 59}{space 1}    1.97{col 68}{space 3}0.049{col 76}{space 4} .0008345{col 89}{space 3} .3224059
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2}   .16516{col 48}{space 2} .0789867{col 59}{space 1}    2.09{col 68}{space 3}0.037{col 76}{space 4} .0103489{col 89}{space 3} .3199711
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2}-.2244365{col 48}{space 2} .0586611{col 59}{space 1}   -3.83{col 68}{space 3}0.000{col 76}{space 4}-.3394101{col 89}{space 3}-.1094629
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2}-.2405031{col 48}{space 2} .0864731{col 59}{space 1}   -2.78{col 68}{space 3}0.005{col 76}{space 4}-.4099873{col 89}{space 3}-.0710189
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2}  9.21233{col 48}{space 2} 3.968814{col 59}{space 1}    2.32{col 68}{space 3}0.020{col 76}{space 4} 1.433598{col 89}{space 3} 16.99106
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2}-2.389807{col 48}{space 2}  .808666{col 59}{space 1}   -2.96{col 68}{space 3}0.003{col 76}{space 4}-3.974763{col 89}{space 3}-.8048505
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .6695276{col 48}{space 2} .0580715{col 59}{space 1}   11.53{col 68}{space 3}0.000{col 76}{space 4} .5557095{col 89}{space 3} .7833457
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} .0036927{col 48}{space 2}  .001676{col 59}{space 1}    2.20{col 68}{space 3}0.028{col 76}{space 4} .0004078{col 89}{space 3} .0069777
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} -.051589{col 48}{space 2} .0292518{col 59}{space 1}   -1.76{col 68}{space 3}0.078{col 76}{space 4}-.1089214{col 89}{space 3} .0057434
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2}        0{col 48}{txt}  (omitted)
{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2}-.1961703{col 48}{space 2} .0766071{col 59}{space 1}   -2.56{col 68}{space 3}0.010{col 76}{space 4}-.3463175{col 89}{space 3} -.046023
{txt}{space 32}3  {c |}{col 36}{res}{space 2}-.5612073{col 48}{space 2} .0740729{col 59}{space 1}   -7.58{col 68}{space 3}0.000{col 76}{space 4}-.7063874{col 89}{space 3}-.4160271
{txt}{space 32}4  {c |}{col 36}{res}{space 2}-.5741363{col 48}{space 2} .1145376{col 59}{space 1}   -5.01{col 68}{space 3}0.000{col 76}{space 4}-.7986258{col 89}{space 3}-.3496468
{txt}{space 32}5  {c |}{col 36}{res}{space 2}-.1621519{col 48}{space 2} .0956518{col 59}{space 1}   -1.70{col 68}{space 3}0.090{col 76}{space 4} -.349626{col 89}{space 3} .0253222
{txt}{space 32}6  {c |}{col 36}{res}{space 2}-.4554936{col 48}{space 2} .0958899{col 59}{space 1}   -4.75{col 68}{space 3}0.000{col 76}{space 4}-.6434343{col 89}{space 3}-.2675528
{txt}{space 32}7  {c |}{col 36}{res}{space 2}-.6941344{col 48}{space 2}  .095292{col 59}{space 1}   -7.28{col 68}{space 3}0.000{col 76}{space 4}-.8809033{col 89}{space 3}-.5073654
{txt}{space 32}8  {c |}{col 36}{res}{space 2}-.9668553{col 48}{space 2}  .149775{col 59}{space 1}   -6.46{col 68}{space 3}0.000{col 76}{space 4}-1.260409{col 89}{space 3}-.6733017
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2}-.4210932{col 48}{space 2} .0822755{col 59}{space 1}   -5.12{col 68}{space 3}0.000{col 76}{space 4}-.5823502{col 89}{space 3}-.2598361
{txt}{space 32}3  {c |}{col 36}{res}{space 2}-.2682373{col 48}{space 2} .0854792{col 59}{space 1}   -3.14{col 68}{space 3}0.002{col 76}{space 4}-.4357734{col 89}{space 3}-.1007012
{txt}{space 32}4  {c |}{col 36}{res}{space 2}-.0924535{col 48}{space 2} .0691776{col 59}{space 1}   -1.34{col 68}{space 3}0.181{col 76}{space 4}-.2280391{col 89}{space 3}  .043132
{txt}{space 32}5  {c |}{col 36}{res}{space 2}-.0414566{col 48}{space 2} .0886375{col 59}{space 1}   -0.47{col 68}{space 3}0.640{col 76}{space 4}-.2151828{col 89}{space 3} .1322697
{txt}{space 32}6  {c |}{col 36}{res}{space 2}-.3352285{col 48}{space 2} .0750417{col 59}{space 1}   -4.47{col 68}{space 3}0.000{col 76}{space 4}-.4823075{col 89}{space 3}-.1881495
{txt}{space 32}7  {c |}{col 36}{res}{space 2}-.2389678{col 48}{space 2} .0839795{col 59}{space 1}   -2.85{col 68}{space 3}0.004{col 76}{space 4}-.4035646{col 89}{space 3} -.074371
{txt}{space 32}8  {c |}{col 36}{res}{space 2}-.3593615{col 48}{space 2} .0846381{col 59}{space 1}   -4.25{col 68}{space 3}0.000{col 76}{space 4}-.5252492{col 89}{space 3}-.1934739
{txt}{space 32}9  {c |}{col 36}{res}{space 2}-.2970291{col 48}{space 2} .0758347{col 59}{space 1}   -3.92{col 68}{space 3}0.000{col 76}{space 4}-.4456625{col 89}{space 3}-.1483958
{txt}{space 31}11  {c |}{col 36}{res}{space 2}-.5405777{col 48}{space 2} .1016577{col 59}{space 1}   -5.32{col 68}{space 3}0.000{col 76}{space 4}-.7398231{col 89}{space 3}-.3413323
{txt}{space 31}12  {c |}{col 36}{res}{space 2}-.2428016{col 48}{space 2} .0614505{col 59}{space 1}   -3.95{col 68}{space 3}0.000{col 76}{space 4}-.3632424{col 89}{space 3}-.1223608
{txt}{space 31}13  {c |}{col 36}{res}{space 2}-.1824223{col 48}{space 2} .0742363{col 59}{space 1}   -2.46{col 68}{space 3}0.014{col 76}{space 4}-.3279228{col 89}{space 3}-.0369219
{txt}{space 31}14  {c |}{col 36}{res}{space 2}-.3748228{col 48}{space 2} .0883689{col 59}{space 1}   -4.24{col 68}{space 3}0.000{col 76}{space 4}-.5480227{col 89}{space 3}-.2016229
{txt}{space 31}15  {c |}{col 36}{res}{space 2} -.212549{col 48}{space 2} .0780329{col 59}{space 1}   -2.72{col 68}{space 3}0.006{col 76}{space 4}-.3654907{col 89}{space 3}-.0596073
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .0467606{col 48}{space 2} .0531651{col 59}{space 1}    0.88{col 68}{space 3}0.379{col 76}{space 4} -.057441{col 89}{space 3} .1509623
{txt}{space 31}17  {c |}{col 36}{res}{space 2}-.1585921{col 48}{space 2}  .027247{col 59}{space 1}   -5.82{col 68}{space 3}0.000{col 76}{space 4}-.2119953{col 89}{space 3}-.1051889
{txt}{space 31}18  {c |}{col 36}{res}{space 2}-.2834553{col 48}{space 2} .0859214{col 59}{space 1}   -3.30{col 68}{space 3}0.001{col 76}{space 4}-.4518582{col 89}{space 3}-.1150525
{txt}{space 31}19  {c |}{col 36}{res}{space 2} .0439461{col 48}{space 2} .0461042{col 59}{space 1}    0.95{col 68}{space 3}0.340{col 76}{space 4}-.0464164{col 89}{space 3} .1343086
{txt}{space 31}20  {c |}{col 36}{res}{space 2}   .40626{col 48}{space 2} .0746071{col 59}{space 1}    5.45{col 68}{space 3}0.000{col 76}{space 4} .2600328{col 89}{space 3} .5524873
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .0429868{col 48}{space 2} .0263002{col 59}{space 1}    1.63{col 68}{space 3}0.102{col 76}{space 4}-.0085606{col 89}{space 3} .0945342
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .2389109{col 48}{space 2}   .08977{col 59}{space 1}    2.66{col 68}{space 3}0.008{col 76}{space 4}  .062965{col 89}{space 3} .4148569
{txt}{space 31}23  {c |}{col 36}{res}{space 2}-.0044396{col 48}{space 2} .0791682{col 59}{space 1}   -0.06{col 68}{space 3}0.955{col 76}{space 4}-.1596065{col 89}{space 3} .1507272
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .3837129{col 48}{space 2} .1008601{col 59}{space 1}    3.80{col 68}{space 3}0.000{col 76}{space 4} .1860307{col 89}{space 3}  .581395
{txt}{space 31}25  {c |}{col 36}{res}{space 2}-.1612319{col 48}{space 2} .0451315{col 59}{space 1}   -3.57{col 68}{space 3}0.000{col 76}{space 4}-.2496879{col 89}{space 3}-.0727758
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .0565336{col 48}{space 2} .0394964{col 59}{space 1}    1.43{col 68}{space 3}0.152{col 76}{space 4}-.0208778{col 89}{space 3} .1339451
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        0{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} -.106509{col 48}{space 2} .0351506{col 59}{space 1}   -3.03{col 68}{space 3}0.002{col 76}{space 4}-.1754029{col 89}{space 3}-.0376151
{txt}{space 31}29  {c |}{col 36}{res}{space 2}-.5082465{col 48}{space 2} .1097115{col 59}{space 1}   -4.63{col 68}{space 3}0.000{col 76}{space 4}-.7232771{col 89}{space 3} -.293216
{txt}{space 31}30  {c |}{col 36}{res}{space 2}-.1638189{col 48}{space 2} .0993762{col 59}{space 1}   -1.65{col 68}{space 3}0.099{col 76}{space 4}-.3585927{col 89}{space 3} .0309548
{txt}{space 31}50  {c |}{col 36}{res}{space 2}-.2429277{col 48}{space 2} .0649935{col 59}{space 1}   -3.74{col 68}{space 3}0.000{col 76}{space 4}-.3703126{col 89}{space 3}-.1155429
{txt}{space 31}51  {c |}{col 36}{res}{space 2}-.3917532{col 48}{space 2} .0779355{col 59}{space 1}   -5.03{col 68}{space 3}0.000{col 76}{space 4}-.5445039{col 89}{space 3}-.2390025
{txt}{space 31}52  {c |}{col 36}{res}{space 2}-.2203406{col 48}{space 2}  .105014{col 59}{space 1}   -2.10{col 68}{space 3}0.036{col 76}{space 4}-.4261643{col 89}{space 3}-.0145169
{txt}{space 31}53  {c |}{col 36}{res}{space 2}-.1183612{col 48}{space 2} .0359069{col 59}{space 1}   -3.30{col 68}{space 3}0.001{col 76}{space 4}-.1887374{col 89}{space 3}-.0479851
{txt}{space 31}54  {c |}{col 36}{res}{space 2}-.1942737{col 48}{space 2} .0635089{col 59}{space 1}   -3.06{col 68}{space 3}0.002{col 76}{space 4}-.3187488{col 89}{space 3}-.0697987
{txt}{space 31}55  {c |}{col 36}{res}{space 2}-.0272384{col 48}{space 2} .1112759{col 59}{space 1}   -0.24{col 68}{space 3}0.807{col 76}{space 4}-.2453351{col 89}{space 3} .1908583
{txt}{space 31}56  {c |}{col 36}{res}{space 2}-.0061127{col 48}{space 2} .1158285{col 59}{space 1}   -0.05{col 68}{space 3}0.958{col 76}{space 4}-.2331324{col 89}{space 3} .2209069
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        0{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2}-.0601063{col 48}{space 2} .1076572{col 59}{space 1}   -0.56{col 68}{space 3}0.577{col 76}{space 4}-.2711105{col 89}{space 3} .1508979
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .2403492{col 48}{space 2} .0988366{col 59}{space 1}    2.43{col 68}{space 3}0.015{col 76}{space 4}  .046633{col 89}{space 3} .4340655
{txt}{space 31}60  {c |}{col 36}{res}{space 2} .0290663{col 48}{space 2} .0414538{col 59}{space 1}    0.70{col 68}{space 3}0.483{col 76}{space 4}-.0521817{col 89}{space 3} .1103142
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        0{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} .9344565{col 48}{space 2}   .33583{col 59}{space 1}    2.78{col 68}{space 3}0.005{col 76}{space 4} .2762417{col 89}{space 3} 1.592671
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2} .6182726{col 48}{space 2} .2242157{col 59}{space 1}    2.76{col 68}{space 3}0.006{col 76}{space 4} .1788179{col 89}{space 3} 1.057727
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} .0853554{col 48}{space 2} .2059764{col 59}{space 1}    0.41{col 68}{space 3}0.679{col 76}{space 4} -.318351{col 89}{space 3} .4890618
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2} .4377706{col 48}{space 2} .2767022{col 59}{space 1}    1.58{col 68}{space 3}0.114{col 76}{space 4}-.1045557{col 89}{space 3} .9800969
{txt}{space 29}_cons {c |}{col 36}{res}{space 2} 2.611015{col 48}{space 2} 2.144207{col 59}{space 1}    1.22{col 68}{space 3}0.223{col 76}{space 4}-1.591553{col 89}{space 3} 6.813584
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 26}/lnsigma {c |}{col 36}{res}{space 2}-.9285082{col 48}{space 2} .0367376{col 59}{space 1}  -25.27{col 68}{space 3}0.000{col 76}{space 4}-1.000513{col 89}{space 3}-.8565038
{txt}{space 28}/kappa {c |}{col 36}{res}{space 2} .7133796{col 48}{space 2} .1195959{col 59}{space 1}    5.96{col 68}{space 3}0.000{col 76}{space 4} .4789759{col 89}{space 3} .9477832
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                             sigma {c |}{col 36}{res}{space 2} .3951427{col 48}{space 2} .0145166{col 76}{space 4} .3676909{col 89}{space 3} .4246441
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       860{col 28}-820.1447{col 39}-494.4256{col 50}    25{col 58} 1038.851{col 69} 1157.774
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. 
. *** COMPUTE Figure A1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}STANDALONE − NON-STANDALONE Difference{c )-} {c -(}{c -(}2 [M2 & M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQ = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3653231, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3653231

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} 1.257602{col 26}{space 2} .0873027{col 37}{space 1}    3.30{col 46}{space 3}0.001{col 54}{space 4} 1.097622{col 67}{space 3} 1.440899
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelA12zloyal = r(table)
{txt}
{com}. mat list modelA12zloyal
{res}
{txt}modelA12zloyal[9,1]
              (1)
     b {res} 1.2576017
{txt}    se {res} .08730275
{txt}     z {res} 3.3017345
{txt}pvalue {res} .00096089
{txt}    ll {res} 1.0976217
{txt}    ul {res} 1.4408991
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         1
{reset}
{com}. 
. 
. 
. 
. **** COMPUTE Figure A2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQR = 1.3653231 [0.9692858 - (-0.3960373)]
. 
. drop loyalppdiff
{txt}
{com}. generate loyalppdiff = soubinaryagency2nom*zloyalmedian
{txt}
{com}. 
. ** Re-Estimate Model A12  with 'manual' interaction variable **
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.sbagency reagan bush41 clinton bush43, distribution(ggamma) hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1012.6928}  
Iteration 1:{space 3}log pseudolikelihood = {res:-823.39319}  
Iteration 2:{space 3}log pseudolikelihood = {res:-820.15856}  
Iteration 3:{space 3}log pseudolikelihood = {res:-820.14466}  
Iteration 4:{space 3}log pseudolikelihood = {res:-820.14466}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-820.14466}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-610.96571}  
Iteration 2:{space 3}log pseudolikelihood = {res:-502.97484}  
Iteration 3:{space 3}log pseudolikelihood = {res:-494.51433}  
Iteration 4:{space 3}log pseudolikelihood = {res:-494.42565}  
Iteration 5:{space 3}log pseudolikelihood = {res:-494.42559}  
Iteration 6:{space 3}log pseudolikelihood = {res:-494.42559}  
{res}
{txt}Generalized gamma AFT regression

No. of subjects      = {res}         860             {txt}Number of obs    =  {res}       860
{txt}No. of failures      = {res}         831
{txt}Time at risk         = {res}      850034
{col 49}{help j_robustsingular##|_new:Wald chi2(22)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res}-494.42559             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |}      Coef.{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2}-.1217118{col 40}{space 2} .0417528{col 51}{space 1}   -2.92{col 60}{space 3}0.004{col 68}{space 4}-.2035458{col 81}{space 3}-.0398777
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2}-.0649184{col 40}{space 2} .0632468{col 51}{space 1}   -1.03{col 60}{space 3}0.305{col 68}{space 4}-.1888798{col 81}{space 3}  .059043
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .1678771{col 40}{space 2} .0508451{col 51}{space 1}    3.30{col 60}{space 3}0.001{col 68}{space 4} .0682225{col 81}{space 3} .2675317
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2}-.0108729{col 40}{space 2} .0266452{col 51}{space 1}   -0.41{col 60}{space 3}0.683{col 68}{space 4}-.0630966{col 81}{space 3} .0413507
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .0054568{col 40}{space 2} .0242518{col 51}{space 1}    0.23{col 60}{space 3}0.822{col 68}{space 4}-.0420758{col 81}{space 3} .0529894
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .2418702{col 40}{space 2} .0394788{col 51}{space 1}    6.13{col 60}{space 3}0.000{col 68}{space 4} .1644932{col 81}{space 3} .3192472
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} .1616202{col 40}{space 2}  .082035{col 51}{space 1}    1.97{col 60}{space 3}0.049{col 68}{space 4} .0008345{col 81}{space 3} .3224059
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2}   .16516{col 40}{space 2} .0789867{col 51}{space 1}    2.09{col 60}{space 3}0.037{col 68}{space 4} .0103489{col 81}{space 3} .3199711
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2}-.2244365{col 40}{space 2} .0586611{col 51}{space 1}   -3.83{col 60}{space 3}0.000{col 68}{space 4}-.3394101{col 81}{space 3}-.1094629
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2}-.2405031{col 40}{space 2} .0864731{col 51}{space 1}   -2.78{col 60}{space 3}0.005{col 68}{space 4}-.4099873{col 81}{space 3}-.0710189
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2}  9.21233{col 40}{space 2} 3.968814{col 51}{space 1}    2.32{col 60}{space 3}0.020{col 68}{space 4} 1.433598{col 81}{space 3} 16.99106
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2}-2.389807{col 40}{space 2}  .808666{col 51}{space 1}   -2.96{col 60}{space 3}0.003{col 68}{space 4}-3.974763{col 81}{space 3}-.8048505
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2} .6695276{col 40}{space 2} .0580715{col 51}{space 1}   11.53{col 60}{space 3}0.000{col 68}{space 4} .5557095{col 81}{space 3} .7833457
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .0036927{col 40}{space 2}  .001676{col 51}{space 1}    2.20{col 60}{space 3}0.028{col 68}{space 4} .0004078{col 81}{space 3} .0069777
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} -.051589{col 40}{space 2} .0292518{col 51}{space 1}   -1.76{col 60}{space 3}0.078{col 68}{space 4}-.1089214{col 81}{space 3} .0057434
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2}-.1961703{col 40}{space 2} .0766071{col 51}{space 1}   -2.56{col 60}{space 3}0.010{col 68}{space 4}-.3463175{col 81}{space 3} -.046023
{txt}{space 24}3  {c |}{col 28}{res}{space 2}-.5612073{col 40}{space 2} .0740729{col 51}{space 1}   -7.58{col 60}{space 3}0.000{col 68}{space 4}-.7063874{col 81}{space 3}-.4160271
{txt}{space 24}4  {c |}{col 28}{res}{space 2}-.5741363{col 40}{space 2} .1145376{col 51}{space 1}   -5.01{col 60}{space 3}0.000{col 68}{space 4}-.7986258{col 81}{space 3}-.3496468
{txt}{space 24}5  {c |}{col 28}{res}{space 2}-.1621519{col 40}{space 2} .0956518{col 51}{space 1}   -1.70{col 60}{space 3}0.090{col 68}{space 4} -.349626{col 81}{space 3} .0253222
{txt}{space 24}6  {c |}{col 28}{res}{space 2}-.4554936{col 40}{space 2} .0958899{col 51}{space 1}   -4.75{col 60}{space 3}0.000{col 68}{space 4}-.6434343{col 81}{space 3}-.2675528
{txt}{space 24}7  {c |}{col 28}{res}{space 2}-.6941344{col 40}{space 2}  .095292{col 51}{space 1}   -7.28{col 60}{space 3}0.000{col 68}{space 4}-.8809033{col 81}{space 3}-.5073654
{txt}{space 24}8  {c |}{col 28}{res}{space 2}-.9668553{col 40}{space 2}  .149775{col 51}{space 1}   -6.46{col 60}{space 3}0.000{col 68}{space 4}-1.260409{col 81}{space 3}-.6733017
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2}-.4210932{col 40}{space 2} .0822755{col 51}{space 1}   -5.12{col 60}{space 3}0.000{col 68}{space 4}-.5823502{col 81}{space 3}-.2598361
{txt}{space 24}3  {c |}{col 28}{res}{space 2}-.2682373{col 40}{space 2} .0854792{col 51}{space 1}   -3.14{col 60}{space 3}0.002{col 68}{space 4}-.4357734{col 81}{space 3}-.1007012
{txt}{space 24}4  {c |}{col 28}{res}{space 2}-.0924535{col 40}{space 2} .0691776{col 51}{space 1}   -1.34{col 60}{space 3}0.181{col 68}{space 4}-.2280391{col 81}{space 3}  .043132
{txt}{space 24}5  {c |}{col 28}{res}{space 2}-.0414566{col 40}{space 2} .0886375{col 51}{space 1}   -0.47{col 60}{space 3}0.640{col 68}{space 4}-.2151828{col 81}{space 3} .1322697
{txt}{space 24}6  {c |}{col 28}{res}{space 2}-.3352285{col 40}{space 2} .0750417{col 51}{space 1}   -4.47{col 60}{space 3}0.000{col 68}{space 4}-.4823075{col 81}{space 3}-.1881495
{txt}{space 24}7  {c |}{col 28}{res}{space 2}-.2389678{col 40}{space 2} .0839795{col 51}{space 1}   -2.85{col 60}{space 3}0.004{col 68}{space 4}-.4035646{col 81}{space 3} -.074371
{txt}{space 24}8  {c |}{col 28}{res}{space 2}-.3593615{col 40}{space 2} .0846381{col 51}{space 1}   -4.25{col 60}{space 3}0.000{col 68}{space 4}-.5252492{col 81}{space 3}-.1934739
{txt}{space 24}9  {c |}{col 28}{res}{space 2}-.2970291{col 40}{space 2} .0758347{col 51}{space 1}   -3.92{col 60}{space 3}0.000{col 68}{space 4}-.4456625{col 81}{space 3}-.1483958
{txt}{space 23}11  {c |}{col 28}{res}{space 2}-.5405777{col 40}{space 2} .1016577{col 51}{space 1}   -5.32{col 60}{space 3}0.000{col 68}{space 4}-.7398231{col 81}{space 3}-.3413323
{txt}{space 23}12  {c |}{col 28}{res}{space 2}-.2428016{col 40}{space 2} .0614505{col 51}{space 1}   -3.95{col 60}{space 3}0.000{col 68}{space 4}-.3632424{col 81}{space 3}-.1223608
{txt}{space 23}13  {c |}{col 28}{res}{space 2}-.1824223{col 40}{space 2} .0742363{col 51}{space 1}   -2.46{col 60}{space 3}0.014{col 68}{space 4}-.3279228{col 81}{space 3}-.0369219
{txt}{space 23}14  {c |}{col 28}{res}{space 2}-.3748228{col 40}{space 2} .0883689{col 51}{space 1}   -4.24{col 60}{space 3}0.000{col 68}{space 4}-.5480227{col 81}{space 3}-.2016229
{txt}{space 23}15  {c |}{col 28}{res}{space 2} -.212549{col 40}{space 2} .0780329{col 51}{space 1}   -2.72{col 60}{space 3}0.006{col 68}{space 4}-.3654907{col 81}{space 3}-.0596073
{txt}{space 23}16  {c |}{col 28}{res}{space 2} .0467606{col 40}{space 2} .0531651{col 51}{space 1}    0.88{col 60}{space 3}0.379{col 68}{space 4} -.057441{col 81}{space 3} .1509623
{txt}{space 23}17  {c |}{col 28}{res}{space 2}-.1585921{col 40}{space 2}  .027247{col 51}{space 1}   -5.82{col 60}{space 3}0.000{col 68}{space 4}-.2119953{col 81}{space 3}-.1051889
{txt}{space 23}18  {c |}{col 28}{res}{space 2}-.2834553{col 40}{space 2} .0859214{col 51}{space 1}   -3.30{col 60}{space 3}0.001{col 68}{space 4}-.4518582{col 81}{space 3}-.1150525
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .0439461{col 40}{space 2} .0461042{col 51}{space 1}    0.95{col 60}{space 3}0.340{col 68}{space 4}-.0464164{col 81}{space 3} .1343086
{txt}{space 23}20  {c |}{col 28}{res}{space 2}   .40626{col 40}{space 2} .0746071{col 51}{space 1}    5.45{col 60}{space 3}0.000{col 68}{space 4} .2600328{col 81}{space 3} .5524873
{txt}{space 23}21  {c |}{col 28}{res}{space 2} .0429868{col 40}{space 2} .0263002{col 51}{space 1}    1.63{col 60}{space 3}0.102{col 68}{space 4}-.0085606{col 81}{space 3} .0945342
{txt}{space 23}22  {c |}{col 28}{res}{space 2} .2389109{col 40}{space 2}   .08977{col 51}{space 1}    2.66{col 60}{space 3}0.008{col 68}{space 4}  .062965{col 81}{space 3} .4148569
{txt}{space 23}23  {c |}{col 28}{res}{space 2}-.0044396{col 40}{space 2} .0791682{col 51}{space 1}   -0.06{col 60}{space 3}0.955{col 68}{space 4}-.1596065{col 81}{space 3} .1507272
{txt}{space 23}24  {c |}{col 28}{res}{space 2} .3837129{col 40}{space 2} .1008601{col 51}{space 1}    3.80{col 60}{space 3}0.000{col 68}{space 4} .1860307{col 81}{space 3}  .581395
{txt}{space 23}25  {c |}{col 28}{res}{space 2}-.1612319{col 40}{space 2} .0451315{col 51}{space 1}   -3.57{col 60}{space 3}0.000{col 68}{space 4}-.2496879{col 81}{space 3}-.0727758
{txt}{space 23}26  {c |}{col 28}{res}{space 2} .0565336{col 40}{space 2} .0394964{col 51}{space 1}    1.43{col 60}{space 3}0.152{col 68}{space 4}-.0208778{col 81}{space 3} .1339451
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} -.106509{col 40}{space 2} .0351506{col 51}{space 1}   -3.03{col 60}{space 3}0.002{col 68}{space 4}-.1754029{col 81}{space 3}-.0376151
{txt}{space 23}29  {c |}{col 28}{res}{space 2}-.5082465{col 40}{space 2} .1097115{col 51}{space 1}   -4.63{col 60}{space 3}0.000{col 68}{space 4}-.7232771{col 81}{space 3} -.293216
{txt}{space 23}30  {c |}{col 28}{res}{space 2}-.1638189{col 40}{space 2} .0993762{col 51}{space 1}   -1.65{col 60}{space 3}0.099{col 68}{space 4}-.3585927{col 81}{space 3} .0309548
{txt}{space 23}50  {c |}{col 28}{res}{space 2}-.2429277{col 40}{space 2} .0649935{col 51}{space 1}   -3.74{col 60}{space 3}0.000{col 68}{space 4}-.3703126{col 81}{space 3}-.1155429
{txt}{space 23}51  {c |}{col 28}{res}{space 2}-.3917532{col 40}{space 2} .0779355{col 51}{space 1}   -5.03{col 60}{space 3}0.000{col 68}{space 4}-.5445039{col 81}{space 3}-.2390025
{txt}{space 23}52  {c |}{col 28}{res}{space 2}-.2203406{col 40}{space 2}  .105014{col 51}{space 1}   -2.10{col 60}{space 3}0.036{col 68}{space 4}-.4261643{col 81}{space 3}-.0145169
{txt}{space 23}53  {c |}{col 28}{res}{space 2}-.1183612{col 40}{space 2} .0359069{col 51}{space 1}   -3.30{col 60}{space 3}0.001{col 68}{space 4}-.1887374{col 81}{space 3}-.0479851
{txt}{space 23}54  {c |}{col 28}{res}{space 2}-.1942737{col 40}{space 2} .0635089{col 51}{space 1}   -3.06{col 60}{space 3}0.002{col 68}{space 4}-.3187488{col 81}{space 3}-.0697987
{txt}{space 23}55  {c |}{col 28}{res}{space 2}-.0272384{col 40}{space 2} .1112759{col 51}{space 1}   -0.24{col 60}{space 3}0.807{col 68}{space 4}-.2453351{col 81}{space 3} .1908583
{txt}{space 23}56  {c |}{col 28}{res}{space 2}-.0061127{col 40}{space 2} .1158285{col 51}{space 1}   -0.05{col 60}{space 3}0.958{col 68}{space 4}-.2331324{col 81}{space 3} .2209069
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2}-.0601063{col 40}{space 2} .1076572{col 51}{space 1}   -0.56{col 60}{space 3}0.577{col 68}{space 4}-.2711105{col 81}{space 3} .1508979
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .2403492{col 40}{space 2} .0988366{col 51}{space 1}    2.43{col 60}{space 3}0.015{col 68}{space 4}  .046633{col 81}{space 3} .4340655
{txt}{space 23}60  {c |}{col 28}{res}{space 2} .0290663{col 40}{space 2} .0414538{col 51}{space 1}    0.70{col 60}{space 3}0.483{col 68}{space 4}-.0521817{col 81}{space 3} .1103142
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        0{col 40}{txt}  (omitted)
{space 26} {c |}
{space 20}reagan {c |}{col 28}{res}{space 2} .9344565{col 40}{space 2}   .33583{col 51}{space 1}    2.78{col 60}{space 3}0.005{col 68}{space 4} .2762417{col 81}{space 3} 1.592671
{txt}{space 20}bush41 {c |}{col 28}{res}{space 2} .6182726{col 40}{space 2} .2242157{col 51}{space 1}    2.76{col 60}{space 3}0.006{col 68}{space 4} .1788179{col 81}{space 3} 1.057727
{txt}{space 19}clinton {c |}{col 28}{res}{space 2} .0853554{col 40}{space 2} .2059764{col 51}{space 1}    0.41{col 60}{space 3}0.679{col 68}{space 4} -.318351{col 81}{space 3} .4890618
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2} .4377706{col 40}{space 2} .2767022{col 51}{space 1}    1.58{col 60}{space 3}0.114{col 68}{space 4}-.1045557{col 81}{space 3} .9800969
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} 2.611015{col 40}{space 2} 2.144207{col 51}{space 1}    1.22{col 60}{space 3}0.223{col 68}{space 4}-1.591553{col 81}{space 3} 6.813584
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 18}/lnsigma {c |}{col 28}{res}{space 2}-.9285082{col 40}{space 2} .0367376{col 51}{space 1}  -25.27{col 60}{space 3}0.000{col 68}{space 4}-1.000513{col 81}{space 3}-.8565038
{txt}{space 20}/kappa {c |}{col 28}{res}{space 2} .7133796{col 40}{space 2} .1195959{col 51}{space 1}    5.96{col 60}{space 3}0.000{col 68}{space 4} .4789759{col 81}{space 3} .9477832
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                     sigma {c |}{col 28}{res}{space 2} .3951427{col 40}{space 2} .0145166{col 68}{space 4} .3676909{col 81}{space 3} .4246441
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. estimates store modela12
{txt}
{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  907.709{col 26}{space 2}  25.1253{col 37}{space 1}   36.13{col 46}{space 3}0.000{col 54}{space 4} 858.4643{col 67}{space 3} 956.9537
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1141.536{col 26}{space 2} 55.51374{col 37}{space 1}   20.56{col 46}{space 3}0.000{col 54}{space 4} 1032.731{col 67}{space 3} 1250.341
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. 
. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9692858))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9692858}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}    10.01{col 38}{space 2}   0.0016
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 233.8274{col 26}{space 2} 73.90635{col 37}{space 5}  88.9736{col 51}{space 3} 378.6812
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelA12azloyal = r(table)
{txt}
{com}. mat list modelA12azloyal
{res}
{txt}modelA12azloyal[9,1]
            r2vs1.
              _at
     b {res} 233.82739
{txt}    se {res} 73.906352
{txt}     z {res} 3.1638334
{txt}pvalue {res} .00155706
{txt}    ll {res} 88.973597
{txt}    ul {res} 378.68117
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. estimates restore modela12
{txt}(results {stata estimates replay modela12:modela12} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2}  870.529{col 26}{space 2} 33.87511{col 37}{space 1}   25.70{col 46}{space 3}0.000{col 54}{space 4}  804.135{col 67}{space 3}  936.923
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1292.981{col 26}{space 2} 110.2959{col 37}{space 1}   11.72{col 46}{space 3}0.000{col 54}{space 4} 1076.805{col 67}{space 3} 1509.157
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-0.6451644 1.711348))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       860
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6451644}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.711348}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     9.12{col 38}{space 2}   0.0025
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 422.4519{col 26}{space 2} 139.9171{col 37}{space 5} 148.2195{col 51}{space 3} 696.6843
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelA12bzloyal = r(table)
{txt}
{com}. mat list modelA12bzloyal
{res}
{txt}modelA12bzloyal[9,1]
            r2vs1.
              _at
     b {res} 422.45187
{txt}    se {res} 139.91707
{txt}     z {res}  3.019302
{txt}pvalue {res} .00253358
{txt}    ll {res} 148.21946
{txt}    ul {res} 696.68429
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. **************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. *******************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. 
. **** ALTERNATIVE ESTIMATION OF REPORTED MODELS --  A2: COMPETING RISKS MODEL  ***
. 
. *** GENERATE BINARY INDICATOR WHETHER SUBSEQUENT POSITION WAS IN A DIFFERENT PRESIDENTIAL-APPOINTED POSITION [POSTEMPLOYMENT <= 3] VERSUS ///
> *** A NON-PRESIDENTIAL APPOINTED POSITION [3 < POSTEMPLOYMENT < 7], WITH 33 MISSING CASES/COULD NOT LOCATE/DETERMINE SUBSEQUENT POSITION CONSTITUTING 3.84% OF FULL SAMPLE. 
. 
. generate postemploymentpres = 1 if postemployment <= 3
{txt}(760 missing values generated)

{com}. replace postemploymentpres = 0 if postemployment > 3
{txt}(760 real changes made)

{com}. 
. *** PLREIMINARY STATISTICAL ANALYSIS TO DEMONSTRATE THAT APPOINTEE TENURE DURATION DOES NOT SYSTEMATICALLY DIFFER BETWEEN THE TWO GROUPS ***
. 
. ** FIRST, EQUALITY OF MEANS (t) TEST ***
. 
. ttest okapptdur, by(postemploymentpres) reverse unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       1 {c |}{res}{col 12}    100{col 22}   945.84{col 34} 52.23432{col 46} 522.3432{col 58} 842.1958{col 70} 1049.484
       {txt}0 {c |}{res}{col 12}    760{col 22} 994.0132{col 34} 21.60755{col 46} 595.6791{col 58} 951.5955{col 70} 1036.431
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    860{col 22} 988.4116{col 34} 20.03514{col 46} 587.5456{col 58} 949.0881{col 70} 1027.735
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-48.17316{col 34} 56.52708{col 58}-159.9644{col 70} 63.61804
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}1{txt}) - mean({res}0{txt})                                      t = {res} -0.8522
{txt}Ho: diff = 0                     Satterthwaite's degrees of freedom = {res} 135.264

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.1978         {txt}Pr(|T| > |t|) = {res}0.3956          {txt}Pr(T > t) = {res}0.8022
{txt}
{com}. 
. ** SECOND, EQUALITY OF STANDARD DEVIATION [F] TEST **
. 
. sdtest okapptdur, by(postemploymentpres)

{txt}Variance ratio test
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    760{col 22} 994.0132{col 34} 21.60755{col 46} 595.6791{col 58} 951.5955{col 70} 1036.431
       {txt}1 {c |}{res}{col 12}    100{col 22}   945.84{col 34} 52.23432{col 46} 522.3432{col 58} 842.1958{col 70} 1049.484
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    860{col 22} 988.4116{col 34} 20.03514{col 46} 587.5456{col 58} 949.0881{col 70} 1027.735
{txt}{hline 9}{c BT}{hline 68}
    ratio = sd({res}0{txt}) / sd({res}1{txt})                                         f = {res}  1.3005
{txt}Ho: ratio = 1                                    degrees of freedom =  {res}759, 99

    {txt}Ha: ratio < 1               Ha: ratio != 1                 Ha: ratio > 1
  Pr(F < f) = {res}0.9496         {txt}2*Pr(F > f) = {res}0.1008        {txt}   Pr(F > f) = {res}0.0504
{txt}
{com}. 
. ** THIRD, EQUALITY OF DISTRIBUTION FUNCTIONS [NONPARAMETRIC KOLMOGOROV-SMIRNOV TEST] **
. 
. ksmirnov okapptdur, by(postemploymentpres) exact

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value      Exact
 {hline 46}
 0:                {res}  0.0426    0.725
{txt} 1:                {res} -0.0716    0.404
{txt} Combined K-S:     {res}  0.0716    0.756      0.727

{txt}Note: Ties exist in combined dataset;
      there are 645 unique values out of 860 observations.

{com}. 
. 
. ****************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. *** SET SURVIVAL TIME TO ACCOUNT FOR COMPETING RISKS USING POSTEMPLOYMENT==1 AS BASELINE -- APPOINTEE DEPARTS FOR A DIFFERENT PRESIDENTIAL-APPOINTED POSITION ***
. 
. stset okapptdur, failure(postemploymentpres==1)

     {txt}failure event:  {res}postemploymentpres == 1
{txt}obs. time interval:  {res}(0, okapptdur]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}        860{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}        860{txt}  observations remaining, representing
{res}        100{txt}  failures in single-record/single-failure data
{res}    850,034{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}    4,074
{txt}
{com}. 
. 
. **** MODEL A2.1: COMPETING RISKS DURATION MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. *** NOTE: EXCLUDE POSTEMPLOYMENT == 9999 CASES SINCE LACK INFORMATION ON SUBSEQUENT POSITION [N = 33] AS NOTED ABOVE
. 
. stcrreg   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian  toplevel2 presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i. okstartadyr  i.sbagency reagan bush41 clinton bush43 if postemployment!=9999,  compete(postemploymentpres==0) hr vce(cluster sbagency)
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
{res}
         {txt}failure _d:  {res}postemploymentpres == 1
   {txt}analysis time _t:  {res}okapptdur

{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-655.03751}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:  -622.508}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-612.80734}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-612.51785}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-612.51631}  
{res}{txt}Iteration 5:{space 3}log pseudolikelihood = {res:-612.51631}  
{res}
{txt}Competing-risks regression{col 50}No. of obs{col 67}={col 69}{res}       827
{txt}{col 50}No. of subjects{col 67}={col 69}{res}       827
{txt}Failure event{col 16}: {res}postempl~s == 1{txt}{col 50}No. failed{col 67}={col 69}{res}       100
{txt}Competing event{col 16}: {res}postempl~s == 0{txt}{col 50}No. competing{col 67}={col 69}{res}       727
{txt}{col 50}No. censored{col 67}={col 69}{res}         0

{col 50}{help j_robustsingular:Wald chi2(41)}{col 67}{txt}={col 70}{res}        .
{txt}Log pseudolikelihood = {res}-612.51631{col 50}{txt}Prob > chi2{col 67}={col 73}{res}     .

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |}        SHR{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2} 1.171217{col 48}{space 2} .2885539{col 59}{space 1}    0.64{col 68}{space 3}0.521{col 76}{space 4} .7226469{col 89}{space 3}  1.89823
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.581221{col 48}{space 2} .4859174{col 59}{space 1}    1.49{col 68}{space 3}0.136{col 76}{space 4}  .865793{col 89}{space 3} 2.887827
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .9181987{col 48}{space 2} .2453796{col 59}{space 1}   -0.32{col 68}{space 3}0.749{col 76}{space 4} .5438278{col 89}{space 3} 1.550286
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.197889{col 48}{space 2} .3014492{col 59}{space 1}    0.72{col 68}{space 3}0.473{col 76}{space 4} .7314953{col 89}{space 3} 1.961651
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2}  1.03469{col 48}{space 2} .1839264{col 59}{space 1}    0.19{col 68}{space 3}0.848{col 76}{space 4} .7302992{col 89}{space 3} 1.465952
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .5883259{col 48}{space 2} .1567256{col 59}{space 1}   -1.99{col 68}{space 3}0.046{col 76}{space 4} .3490312{col 89}{space 3} .9916803
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .2315839{col 48}{space 2} .0733099{col 59}{space 1}   -4.62{col 68}{space 3}0.000{col 76}{space 4}  .124524{col 89}{space 3} .4306889
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .5933721{col 48}{space 2} .2411396{col 59}{space 1}   -1.28{col 68}{space 3}0.199{col 76}{space 4} .2675522{col 89}{space 3} 1.315969
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 4.313389{col 48}{space 2} 1.422949{col 59}{space 1}    4.43{col 68}{space 3}0.000{col 76}{space 4} 2.259513{col 89}{space 3} 8.234222
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 11.85065{col 48}{space 2}   5.4796{col 59}{space 1}    5.35{col 68}{space 3}0.000{col 76}{space 4} 4.788026{col 89}{space 3} 29.33105
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 9.79e-20{col 48}{space 2} 2.13e-18{col 59}{space 1}   -2.01{col 68}{space 3}0.045{col 76}{space 4} 2.69e-38{col 89}{space 3} .3566269
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} .5373306{col 48}{space 2} 2.287868{col 59}{space 1}   -0.15{col 68}{space 3}0.884{col 76}{space 4} .0001276{col 89}{space 3} 2262.152
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} 1.276863{col 48}{space 2} .3823764{col 59}{space 1}    0.82{col 68}{space 3}0.414{col 76}{space 4} .7099678{col 89}{space 3} 2.296412
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2} 1.010522{col 48}{space 2} .0165124{col 59}{space 1}    0.64{col 68}{space 3}0.522{col 76}{space 4} .9786709{col 89}{space 3} 1.043409
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2}  .917803{col 48}{space 2} .1337418{col 59}{space 1}   -0.59{col 68}{space 3}0.556{col 76}{space 4} .6897834{col 89}{space 3} 1.221198
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} .9443491{col 48}{space 2} .3381455{col 59}{space 1}   -0.16{col 68}{space 3}0.873{col 76}{space 4} .4681022{col 89}{space 3} 1.905129
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 1.223686{col 48}{space 2} .4558599{col 59}{space 1}    0.54{col 68}{space 3}0.588{col 76}{space 4} .5896191{col 89}{space 3} 2.539617
{txt}{space 32}4  {c |}{col 36}{res}{space 2} .5953607{col 48}{space 2} .2679095{col 59}{space 1}   -1.15{col 68}{space 3}0.249{col 76}{space 4} .2464583{col 89}{space 3} 1.438192
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.434456{col 48}{space 2} .8755327{col 59}{space 1}    0.59{col 68}{space 3}0.554{col 76}{space 4} .4336599{col 89}{space 3} 4.744883
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 1.079059{col 48}{space 2} .5879312{col 59}{space 1}    0.14{col 68}{space 3}0.889{col 76}{space 4} .3709053{col 89}{space 3} 3.139258
{txt}{space 32}7  {c |}{col 36}{res}{space 2} .1186478{col 48}{space 2} .1421608{col 59}{space 1}   -1.78{col 68}{space 3}0.075{col 76}{space 4} .0113337{col 89}{space 3} 1.242074
{txt}{space 32}8  {c |}{col 36}{res}{space 2} .4183905{col 48}{space 2} .2842977{col 59}{space 1}   -1.28{col 68}{space 3}0.200{col 76}{space 4} .1104555{col 89}{space 3} 1.584806
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 7.615247{col 48}{space 2} 3.322613{col 59}{space 1}    4.65{col 68}{space 3}0.000{col 76}{space 4}  3.23814{col 89}{space 3} 17.90904
{txt}{space 32}3  {c |}{col 36}{res}{space 2}  5.90729{col 48}{space 2} 2.521429{col 59}{space 1}    4.16{col 68}{space 3}0.000{col 76}{space 4} 2.558981{col 89}{space 3} 13.63671
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 2.849619{col 48}{space 2} 1.250953{col 59}{space 1}    2.39{col 68}{space 3}0.017{col 76}{space 4} 1.205364{col 89}{space 3} 6.736829
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 6.75e-09{col 48}{space 2} 7.44e-09{col 59}{space 1}  -17.05{col 68}{space 3}0.000{col 76}{space 4} 7.75e-10{col 89}{space 3} 5.87e-08
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 4.271152{col 48}{space 2} 1.974334{col 59}{space 1}    3.14{col 68}{space 3}0.002{col 76}{space 4} 1.726149{col 89}{space 3} 10.56846
{txt}{space 32}7  {c |}{col 36}{res}{space 2} .9088986{col 48}{space 2} .4779479{col 59}{space 1}   -0.18{col 68}{space 3}0.856{col 76}{space 4} .3242707{col 89}{space 3} 2.547553
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 5.943125{col 48}{space 2} 2.434207{col 59}{space 1}    4.35{col 68}{space 3}0.000{col 76}{space 4} 2.663034{col 89}{space 3} 13.26335
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 3.550037{col 48}{space 2} 1.850087{col 59}{space 1}    2.43{col 68}{space 3}0.015{col 76}{space 4}   1.2783{col 89}{space 3} 9.859002
{txt}{space 31}11  {c |}{col 36}{res}{space 2} 6.005705{col 48}{space 2} 3.081828{col 59}{space 1}    3.49{col 68}{space 3}0.000{col 76}{space 4} 2.196695{col 89}{space 3} 16.41943
{txt}{space 31}12  {c |}{col 36}{res}{space 2} .9136172{col 48}{space 2} .4389138{col 59}{space 1}   -0.19{col 68}{space 3}0.851{col 76}{space 4} .3563164{col 89}{space 3} 2.342571
{txt}{space 31}13  {c |}{col 36}{res}{space 2} 9.437509{col 48}{space 2} 3.573069{col 59}{space 1}    5.93{col 68}{space 3}0.000{col 76}{space 4} 4.493554{col 89}{space 3} 19.82097
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 3.347549{col 48}{space 2} 1.229539{col 59}{space 1}    3.29{col 68}{space 3}0.001{col 76}{space 4} 1.629614{col 89}{space 3} 6.876528
{txt}{space 31}15  {c |}{col 36}{res}{space 2} .4899643{col 48}{space 2} .2091243{col 59}{space 1}   -1.67{col 68}{space 3}0.095{col 76}{space 4} .2122553{col 89}{space 3}  1.13102
{txt}{space 31}16  {c |}{col 36}{res}{space 2} 2.535421{col 48}{space 2}  .615453{col 59}{space 1}    3.83{col 68}{space 3}0.000{col 76}{space 4} 1.575534{col 89}{space 3} 4.080115
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 3.232709{col 48}{space 2} .5791453{col 59}{space 1}    6.55{col 68}{space 3}0.000{col 76}{space 4} 2.275476{col 89}{space 3} 4.592623
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 3.783567{col 48}{space 2} 1.586844{col 59}{space 1}    3.17{col 68}{space 3}0.002{col 76}{space 4} 1.663044{col 89}{space 3} 8.607936
{txt}{space 31}19  {c |}{col 36}{res}{space 2} 1.820214{col 48}{space 2} .3294194{col 59}{space 1}    3.31{col 68}{space 3}0.001{col 76}{space 4} 1.276654{col 89}{space 3} 2.595204
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .2546683{col 48}{space 2} .0949202{col 59}{space 1}   -3.67{col 68}{space 3}0.000{col 76}{space 4} .1226633{col 89}{space 3} .5287317
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .3400783{col 48}{space 2} .0942197{col 59}{space 1}   -3.89{col 68}{space 3}0.000{col 76}{space 4} .1975837{col 89}{space 3} .5853382
{txt}{space 31}22  {c |}{col 36}{res}{space 2} 6.18e-10{col 48}{space 2} 6.96e-10{col 59}{space 1}  -18.83{col 68}{space 3}0.000{col 76}{space 4} 6.80e-11{col 89}{space 3} 5.62e-09
{txt}{space 31}23  {c |}{col 36}{res}{space 2} .2903647{col 48}{space 2} .1268445{col 59}{space 1}   -2.83{col 68}{space 3}0.005{col 76}{space 4}  .123339{col 89}{space 3} .6835767
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .0530558{col 48}{space 2} .0209853{col 59}{space 1}   -7.42{col 68}{space 3}0.000{col 76}{space 4} .0244374{col 89}{space 3} .1151889
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.521884{col 48}{space 2} .6475101{col 59}{space 1}    0.99{col 68}{space 3}0.324{col 76}{space 4} .6610346{col 89}{space 3} 3.503797
{txt}{space 31}26  {c |}{col 36}{res}{space 2} 5.55e-10{col 48}{space 2} 6.12e-10{col 59}{space 1}  -19.35{col 68}{space 3}0.000{col 76}{space 4} 6.42e-11{col 89}{space 3} 4.81e-09
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2} 9.20e-09{col 48}{space 2} 9.60e-09{col 59}{space 1}  -17.73{col 68}{space 3}0.000{col 76}{space 4} 1.19e-09{col 89}{space 3} 7.12e-08
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 14.11126{col 48}{space 2} 8.231761{col 59}{space 1}    4.54{col 68}{space 3}0.000{col 76}{space 4} 4.498004{col 89}{space 3} 44.27022
{txt}{space 31}30  {c |}{col 36}{res}{space 2}  15.0203{col 48}{space 2} 9.690149{col 59}{space 1}    4.20{col 68}{space 3}0.000{col 76}{space 4} 4.241672{col 89}{space 3} 53.18879
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 2.466237{col 48}{space 2} .9629831{col 59}{space 1}    2.31{col 68}{space 3}0.021{col 76}{space 4} 1.147279{col 89}{space 3} 5.301522
{txt}{space 31}51  {c |}{col 36}{res}{space 2} .7065346{col 48}{space 2} .3975437{col 59}{space 1}   -0.62{col 68}{space 3}0.537{col 76}{space 4} .2345256{col 89}{space 3} 2.128514
{txt}{space 31}52  {c |}{col 36}{res}{space 2} .9859692{col 48}{space 2} .4481036{col 59}{space 1}   -0.03{col 68}{space 3}0.975{col 76}{space 4} .4045843{col 89}{space 3}   2.4028
{txt}{space 31}53  {c |}{col 36}{res}{space 2} .8094069{col 48}{space 2} .2241903{col 59}{space 1}   -0.76{col 68}{space 3}0.445{col 76}{space 4} .4703274{col 89}{space 3} 1.392944
{txt}{space 31}54  {c |}{col 36}{res}{space 2}  4.07293{col 48}{space 2} 1.740293{col 59}{space 1}    3.29{col 68}{space 3}0.001{col 76}{space 4}   1.7628{col 89}{space 3} 9.410458
{txt}{space 31}55  {c |}{col 36}{res}{space 2} 2.49e-09{col 48}{space 2} 2.98e-09{col 59}{space 1}  -16.54{col 68}{space 3}0.000{col 76}{space 4} 2.38e-10{col 89}{space 3} 2.60e-08
{txt}{space 31}56  {c |}{col 36}{res}{space 2} 2.98e-09{col 48}{space 2} 3.45e-09{col 59}{space 1}  -16.94{col 68}{space 3}0.000{col 76}{space 4} 3.07e-10{col 89}{space 3} 2.89e-08
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 1.69e-09{col 48}{space 2} 2.00e-09{col 59}{space 1}  -17.04{col 68}{space 3}0.000{col 76}{space 4} 1.65e-10{col 89}{space 3} 1.72e-08
{txt}{space 31}59  {c |}{col 36}{res}{space 2} 2.65e-09{col 48}{space 2} 2.85e-09{col 59}{space 1}  -18.32{col 68}{space 3}0.000{col 76}{space 4} 3.20e-10{col 89}{space 3} 2.19e-08
{txt}{space 31}60  {c |}{col 36}{res}{space 2} 3.47e-09{col 48}{space 2} 3.54e-09{col 59}{space 1}  -19.06{col 68}{space 3}0.000{col 76}{space 4} 4.68e-10{col 89}{space 3} 2.57e-08
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} 13.32226{col 48}{space 2} 25.95721{col 59}{space 1}    1.33{col 68}{space 3}0.184{col 76}{space 4} .2924749{col 89}{space 3}   606.83
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2} 7.132805{col 48}{space 2} 11.37517{col 59}{space 1}    1.23{col 68}{space 3}0.218{col 76}{space 4} .3131777{col 89}{space 3} 162.4538
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} 13.41632{col 48}{space 2} 15.36459{col 59}{space 1}    2.27{col 68}{space 3}0.023{col 76}{space 4}  1.42175{col 89}{space 3} 126.6028
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2} 69.37251{col 48}{space 2} 163.1385{col 59}{space 1}    1.80{col 68}{space 3}0.071{col 76}{space 4} .6909962{col 89}{space 3} 6964.649
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       827{col 28}        .{col 39}-612.5163{col 50}    63{col 58} 1351.033{col 69} 1648.254
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. 
. *** COMPUTE Figure A1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}STANDALONE − NON-STANDALONE Difference{c )-} {c -(}{c -(}2 [M2 & M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQ = 1.3670962 [0.9710589 - (-0.3960373)] for reduced sample N = 827 **
. 
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3670962, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3670962

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .8898788{col 26}{space 2}  .325111{col 37}{space 1}   -0.32{col 46}{space 3}0.749{col 54}{space 4} .4348608{col 67}{space 3} 1.821006
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelA21zloyal = r(table)
{txt}
{com}. mat list modelA21zloyal
{res}
{txt}modelA21zloyal[9,1]
               (1)
     b {res}  .88987878
{txt}    se {res}  .32511104
{txt}     z {res} -.31934376
{txt}pvalue {res}  .74946585
{txt}    ll {res}  .43486081
{txt}    ul {res}  1.8210062
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. **** COMPUTE Figure A2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQ = 1.3670962 [0.9710589 - (-0.3960373)] for reduced sample N = 827 **
. 
. 
. 
. 
. 
. *** ALTHOUGH THE PREVIOUS RESULTS ARE ESTIMATING SUBHAZARD COMPETING RISKS-- IT IS BASED ON A DIFFERENT SAMPLE SINCE 33 CASES ARE OMITTED DUE TO LACK OF INFORMATION ON SUBSEQUENT POSITION FOLLOWING DEPARTURE FROM APPOINTED POSITION (I.E., POSTEMPLOYMENT==9999), WE ANALYZE M2 FROM THE MANUSCRIPT [COX MODEL] BASED ON N = 827 TO ENSURE THAT THE CORE FINDING HOLDS
. 
. 
. *** FIRST, MUST RESET "STSET" COMMAND TO ACCOUNT FOR STANDARD (NON-COMPETING RISKS) MODEL ***
. 
. ** SET FOR SURVIVAL DATA WITH A SINGLE RECORD PER APPOINTEE OBSERVATION [N = 860: UNCENSORED N = 831; CENSORED N = 29] ** 
. stset okapptdur, failure(singleadmin_service)

     {txt}failure event:  {res}singleadmin_service != 0 & singleadmin_service < .
{txt}obs. time interval:  {res}(0, okapptdur]
{txt} exit on or before:  {res}failure

{txt}{hline 78}
{res}        860{txt}  total observations
{res}          0{txt}  exclusions
{hline 78}
{res}        860{txt}  observations remaining, representing
{res}        831{txt}  failures in single-record/single-failure data
{res}    850,034{txt}  total analysis time at risk and under observation
                                                at risk from t = {res}        0
                                     {txt}earliest observed entry t = {res}        0
                                          {txt}last observed exit t = {res}    4,074
{txt}
{com}. 
. 
. 
. **** MODEL A2.2: COX MODEL [INCLUSION OF BOTH AGENCY AND PRESIDENTIAL ADMINISTRATION FIXED EFFECTS: CLUSTER-ADJUSTED STANDARD ERRORS BY AGENCY] ****
. 
. stcox   c.zloyalmedian##i.soubinaryagency2nom  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp  okstartunemployment  i. okstartadyr  i.sbagency reagan bush41 clinton bush43 if postemployment!=9999,  hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur

{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity
Iteration 0:   log pseudolikelihood = {res}-4577.5796
{txt}Iteration 1:   log pseudolikelihood = {res}-4293.3961
{txt}Iteration 2:   log pseudolikelihood = {res} -4267.913
{txt}Iteration 3:   log pseudolikelihood = {res}-4267.5477
{txt}Iteration 4:   log pseudolikelihood = {res}-4267.5473
{txt}Refining estimates:
Iteration 0:   log pseudolikelihood = {res}-4267.5473

{txt}Cox regression -- Breslow method for ties

No. of subjects      = {res}         827             {txt}Number of obs    =  {res}       827
{txt}No. of failures      = {res}         799
{txt}Time at risk         = {res}      821898
                                                {txt}Wald chi2({res}40{txt})    =  {res}  30525.44
{txt}Log pseudolikelihood =   {res}-4267.5473             {txt}Prob > chi2      =  {res}    0.0000

{txt}{ralign 100:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                                _t{col 36}{c |} Haz. Ratio{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}zloyalmedian {c |}{col 36}{res}{space 2}  1.36545{col 48}{space 2} .1556964{col 59}{space 1}    2.73{col 68}{space 3}0.006{col 76}{space 4} 1.091986{col 89}{space 3} 1.707398
{txt}{space 13}1.soubinaryagency2nom {c |}{col 36}{res}{space 2} 1.197163{col 48}{space 2} .2296964{col 59}{space 1}    0.94{col 68}{space 3}0.348{col 76}{space 4} .8219313{col 89}{space 3} 1.743697
{txt}{space 34} {c |}
soubinaryagency2nom#c.zloyalmedian {c |}
{space 32}1  {c |}{col 36}{res}{space 2} .6249747{col 48}{space 2}  .088049{col 59}{space 1}   -3.34{col 68}{space 3}0.001{col 76}{space 4} .4741783{col 89}{space 3} .8237269
{txt}{space 34} {c |}
{space 21}zpecompmedian {c |}{col 36}{res}{space 2} 1.033201{col 48}{space 2} .0897396{col 59}{space 1}    0.38{col 68}{space 3}0.707{col 76}{space 4} .8714711{col 89}{space 3} 1.224945
{txt}{space 21}zmecompmedian {c |}{col 36}{res}{space 2} .9829455{col 48}{space 2}  .068909{col 59}{space 1}   -0.25{col 68}{space 3}0.806{col 76}{space 4} .8567544{col 89}{space 3} 1.127723
{txt}{space 25}toplevel2 {c |}{col 36}{res}{space 2} .5159414{col 48}{space 2} .0552085{col 59}{space 1}   -6.18{col 68}{space 3}0.000{col 76}{space 4} .4183283{col 89}{space 3} .6363316
{txt}{space 14}presagencyideolalign {c |}{col 36}{res}{space 2} .4370533{col 48}{space 2} .0884104{col 59}{space 1}   -4.09{col 68}{space 3}0.000{col 76}{space 4} .2939998{col 89}{space 3} .6497134
{txt}{space 12}presagencyideolopposed {c |}{col 36}{res}{space 2} .4276637{col 48}{space 2} .0929201{col 59}{space 1}   -3.91{col 68}{space 3}0.000{col 76}{space 4} .2793564{col 89}{space 3} .6547059
{txt}{space 19}subagencydesign {c |}{col 36}{res}{space 2} 2.602704{col 48}{space 2} .3735648{col 59}{space 1}    6.66{col 68}{space 3}0.000{col 76}{space 4} 1.964501{col 89}{space 3} 3.448239
{txt}{space 12}standaloneagencydesign {c |}{col 36}{res}{space 2} 2.988688{col 48}{space 2} .7501787{col 59}{space 1}    4.36{col 68}{space 3}0.000{col 76}{space 4} 1.827359{col 89}{space 3} 4.888069
{txt}{space 8}okstartsenpolarizationmean {c |}{col 36}{res}{space 2} 6.81e-11{col 48}{space 2} 7.01e-10{col 59}{space 1}   -2.27{col 68}{space 3}0.023{col 76}{space 4} 1.18e-19{col 89}{space 3} .0391844
{txt}{space 11}okstartfilipresdistance {c |}{col 36}{res}{space 2} 935.3577{col 48}{space 2} 2144.393{col 59}{space 1}    2.98{col 68}{space 3}0.003{col 76}{space 4} 10.45972{col 89}{space 3}  83644.1
{txt}{space 23}okcrossover {c |}{col 36}{res}{space 2} .1619515{col 48}{space 2} .0356257{col 59}{space 1}   -8.28{col 68}{space 3}0.000{col 76}{space 4} .1052301{col 89}{space 3} .2492472
{txt}{space 20}okstartpresapp {c |}{col 36}{res}{space 2}  .990725{col 48}{space 2} .0044876{col 59}{space 1}   -2.06{col 68}{space 3}0.040{col 76}{space 4} .9819685{col 89}{space 3} .9995596
{txt}{space 15}okstartunemployment {c |}{col 36}{res}{space 2} 1.142233{col 48}{space 2} .1014225{col 59}{space 1}    1.50{col 68}{space 3}0.134{col 76}{space 4}  .959785{col 89}{space 3} 1.359364
{txt}{space 34} {c |}
{space 23}okstartadyr {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 1.579773{col 48}{space 2} .3754585{col 59}{space 1}    1.92{col 68}{space 3}0.054{col 76}{space 4} .9915005{col 89}{space 3} 2.517077
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 4.059731{col 48}{space 2} .9620266{col 59}{space 1}    5.91{col 68}{space 3}0.000{col 76}{space 4} 2.551465{col 89}{space 3} 6.459588
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 3.346607{col 48}{space 2}  1.14616{col 59}{space 1}    3.53{col 68}{space 3}0.000{col 76}{space 4} 1.710336{col 89}{space 3} 6.548289
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.572435{col 48}{space 2} .3644639{col 59}{space 1}    1.95{col 68}{space 3}0.051{col 76}{space 4} .9983398{col 89}{space 3} 2.476662
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 3.571622{col 48}{space 2} .8386741{col 59}{space 1}    5.42{col 68}{space 3}0.000{col 76}{space 4} 2.254186{col 89}{space 3} 5.659021
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 5.468195{col 48}{space 2} 1.695114{col 59}{space 1}    5.48{col 68}{space 3}0.000{col 76}{space 4} 2.978349{col 89}{space 3} 10.03951
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 8.777569{col 48}{space 2} 3.370386{col 59}{space 1}    5.66{col 68}{space 3}0.000{col 76}{space 4} 4.135541{col 89}{space 3} 18.63014
{txt}{space 34} {c |}
{space 26}sbagency {c |}
{space 32}2  {c |}{col 36}{res}{space 2} 4.449982{col 48}{space 2} 1.075012{col 59}{space 1}    6.18{col 68}{space 3}0.000{col 76}{space 4} 2.771582{col 89}{space 3} 7.144779
{txt}{space 32}3  {c |}{col 36}{res}{space 2} 2.892792{col 48}{space 2} .6335941{col 59}{space 1}    4.85{col 68}{space 3}0.000{col 76}{space 4}  1.88314{col 89}{space 3} 4.443771
{txt}{space 32}4  {c |}{col 36}{res}{space 2} 1.264464{col 48}{space 2} .3053006{col 59}{space 1}    0.97{col 68}{space 3}0.331{col 76}{space 4} .7877466{col 89}{space 3} 2.029673
{txt}{space 32}5  {c |}{col 36}{res}{space 2} 1.032618{col 48}{space 2}  .289703{col 59}{space 1}    0.11{col 68}{space 3}0.909{col 76}{space 4} .5958453{col 89}{space 3} 1.789559
{txt}{space 32}6  {c |}{col 36}{res}{space 2} 2.716813{col 48}{space 2} .6647698{col 59}{space 1}    4.08{col 68}{space 3}0.000{col 76}{space 4} 1.681828{col 89}{space 3} 4.388721
{txt}{space 32}7  {c |}{col 36}{res}{space 2} 2.732741{col 48}{space 2} .6964626{col 59}{space 1}    3.94{col 68}{space 3}0.000{col 76}{space 4} 1.658298{col 89}{space 3} 4.503338
{txt}{space 32}8  {c |}{col 36}{res}{space 2} 3.631287{col 48}{space 2} .8514147{col 59}{space 1}    5.50{col 68}{space 3}0.000{col 76}{space 4} 2.293414{col 89}{space 3} 5.749614
{txt}{space 32}9  {c |}{col 36}{res}{space 2} 3.250988{col 48}{space 2}  .789542{col 59}{space 1}    4.85{col 68}{space 3}0.000{col 76}{space 4} 2.019718{col 89}{space 3} 5.232871
{txt}{space 31}11  {c |}{col 36}{res}{space 2} 6.014487{col 48}{space 2} 1.719135{col 59}{space 1}    6.28{col 68}{space 3}0.000{col 76}{space 4}  3.43477{col 89}{space 3} 10.53173
{txt}{space 31}12  {c |}{col 36}{res}{space 2} 2.694308{col 48}{space 2} .4019302{col 59}{space 1}    6.64{col 68}{space 3}0.000{col 76}{space 4} 2.011255{col 89}{space 3} 3.609337
{txt}{space 31}13  {c |}{col 36}{res}{space 2} 2.447315{col 48}{space 2} .5051589{col 59}{space 1}    4.34{col 68}{space 3}0.000{col 76}{space 4} 1.633016{col 89}{space 3} 3.667663
{txt}{space 31}14  {c |}{col 36}{res}{space 2} 3.935077{col 48}{space 2} .9818746{col 59}{space 1}    5.49{col 68}{space 3}0.000{col 76}{space 4}  2.41303{col 89}{space 3} 6.417174
{txt}{space 31}15  {c |}{col 36}{res}{space 2} 2.395338{col 48}{space 2} .5397789{col 59}{space 1}    3.88{col 68}{space 3}0.000{col 76}{space 4} 1.540112{col 89}{space 3} 3.725472
{txt}{space 31}16  {c |}{col 36}{res}{space 2} .8144952{col 48}{space 2}  .141574{col 59}{space 1}   -1.18{col 68}{space 3}0.238{col 76}{space 4} .5793411{col 89}{space 3} 1.145098
{txt}{space 31}17  {c |}{col 36}{res}{space 2} 1.460523{col 48}{space 2} .1480909{col 59}{space 1}    3.74{col 68}{space 3}0.000{col 76}{space 4} 1.197292{col 89}{space 3} 1.781627
{txt}{space 31}18  {c |}{col 36}{res}{space 2} 3.050803{col 48}{space 2} .7767653{col 59}{space 1}    4.38{col 68}{space 3}0.000{col 76}{space 4} 1.852207{col 89}{space 3}  5.02503
{txt}{space 31}19  {c |}{col 36}{res}{space 2} .7901862{col 48}{space 2} .1252643{col 59}{space 1}   -1.49{col 68}{space 3}0.137{col 76}{space 4} .5791522{col 89}{space 3} 1.078118
{txt}{space 31}20  {c |}{col 36}{res}{space 2} .1644918{col 48}{space 2}  .045702{col 59}{space 1}   -6.50{col 68}{space 3}0.000{col 76}{space 4}  .095422{col 89}{space 3} .2835568
{txt}{space 31}21  {c |}{col 36}{res}{space 2} .7723839{col 48}{space 2} .0769507{col 59}{space 1}   -2.59{col 68}{space 3}0.010{col 76}{space 4} .6353751{col 89}{space 3} .9389366
{txt}{space 31}22  {c |}{col 36}{res}{space 2} .3154463{col 48}{space 2} .1012293{col 59}{space 1}   -3.60{col 68}{space 3}0.000{col 76}{space 4} .1681775{col 89}{space 3} .5916747
{txt}{space 31}23  {c |}{col 36}{res}{space 2} .6843749{col 48}{space 2} .1547063{col 59}{space 1}   -1.68{col 68}{space 3}0.093{col 76}{space 4} .4394159{col 89}{space 3}  1.06589
{txt}{space 31}24  {c |}{col 36}{res}{space 2} .1971423{col 48}{space 2} .0874813{col 59}{space 1}   -3.66{col 68}{space 3}0.000{col 76}{space 4} .0826155{col 89}{space 3}  .470433
{txt}{space 31}25  {c |}{col 36}{res}{space 2} 1.375105{col 48}{space 2} .2192093{col 59}{space 1}    2.00{col 68}{space 3}0.046{col 76}{space 4} 1.006105{col 89}{space 3} 1.879439
{txt}{space 31}26  {c |}{col 36}{res}{space 2} .7611266{col 48}{space 2} .1162546{col 59}{space 1}   -1.79{col 68}{space 3}0.074{col 76}{space 4} .5642145{col 89}{space 3} 1.026761
{txt}{space 31}27  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}28  {c |}{col 36}{res}{space 2}  1.48918{col 48}{space 2} .1393472{col 59}{space 1}    4.26{col 68}{space 3}0.000{col 76}{space 4} 1.239646{col 89}{space 3} 1.788945
{txt}{space 31}29  {c |}{col 36}{res}{space 2} 5.402978{col 48}{space 2}  1.65171{col 59}{space 1}    5.52{col 68}{space 3}0.000{col 76}{space 4} 2.967685{col 89}{space 3} 9.836682
{txt}{space 31}30  {c |}{col 36}{res}{space 2} 2.270993{col 48}{space 2} .6651417{col 59}{space 1}    2.80{col 68}{space 3}0.005{col 76}{space 4} 1.279118{col 89}{space 3} 4.032001
{txt}{space 31}50  {c |}{col 36}{res}{space 2} 2.090628{col 48}{space 2} .4102863{col 59}{space 1}    3.76{col 68}{space 3}0.000{col 76}{space 4} 1.423076{col 89}{space 3} 3.071322
{txt}{space 31}51  {c |}{col 36}{res}{space 2} 3.233242{col 48}{space 2} .9057087{col 59}{space 1}    4.19{col 68}{space 3}0.000{col 76}{space 4} 1.867223{col 89}{space 3}  5.59861
{txt}{space 31}52  {c |}{col 36}{res}{space 2} 1.448875{col 48}{space 2} .4977936{col 59}{space 1}    1.08{col 68}{space 3}0.280{col 76}{space 4} .7388925{col 89}{space 3} 2.841059
{txt}{space 31}53  {c |}{col 36}{res}{space 2} 1.174272{col 48}{space 2} .1649645{col 59}{space 1}    1.14{col 68}{space 3}0.253{col 76}{space 4} .8916407{col 89}{space 3} 1.546491
{txt}{space 31}54  {c |}{col 36}{res}{space 2} 1.696075{col 48}{space 2} .3353675{col 59}{space 1}    2.67{col 68}{space 3}0.008{col 76}{space 4}  1.15116{col 89}{space 3} 2.498932
{txt}{space 31}55  {c |}{col 36}{res}{space 2} 1.194213{col 48}{space 2} .4421703{col 59}{space 1}    0.48{col 68}{space 3}0.632{col 76}{space 4} .5779835{col 89}{space 3} 2.467451
{txt}{space 31}56  {c |}{col 36}{res}{space 2} .9452417{col 48}{space 2}  .363558{col 59}{space 1}   -0.15{col 68}{space 3}0.884{col 76}{space 4} .4447895{col 89}{space 3} 2.008775
{txt}{space 31}57  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 31}58  {c |}{col 36}{res}{space 2} 1.334515{col 48}{space 2} .4247287{col 59}{space 1}    0.91{col 68}{space 3}0.365{col 76}{space 4} .7151816{col 89}{space 3}  2.49018
{txt}{space 31}59  {c |}{col 36}{res}{space 2} .3331389{col 48}{space 2} .1230555{col 59}{space 1}   -2.98{col 68}{space 3}0.003{col 76}{space 4} .1615128{col 89}{space 3} .6871376
{txt}{space 31}60  {c |}{col 36}{res}{space 2} 1.076316{col 48}{space 2} .1649148{col 59}{space 1}    0.48{col 68}{space 3}0.631{col 76}{space 4} .7971086{col 89}{space 3} 1.453323
{txt}{space 31}61  {c |}{col 36}{res}{space 2}        1{col 48}{txt}  (omitted)
{space 34} {c |}
{space 28}reagan {c |}{col 36}{res}{space 2} .0594502{col 48}{space 2} .0575123{col 59}{space 1}   -2.92{col 68}{space 3}0.004{col 76}{space 4} .0089269{col 89}{space 3} .3959211
{txt}{space 28}bush41 {c |}{col 36}{res}{space 2} .1563992{col 48}{space 2} .0976055{col 59}{space 1}   -2.97{col 68}{space 3}0.003{col 76}{space 4} .0460275{col 89}{space 3} .5314372
{txt}{space 27}clinton {c |}{col 36}{res}{space 2} .6032739{col 48}{space 2}  .312817{col 59}{space 1}   -0.97{col 68}{space 3}0.330{col 76}{space 4} .2183428{col 89}{space 3} 1.666825
{txt}{space 28}bush43 {c |}{col 36}{res}{space 2} .2117838{col 48}{space 2} .1577985{col 59}{space 1}   -2.08{col 68}{space 3}0.037{col 76}{space 4} .0491665{col 89}{space 3} .9122555
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. *
. estat ic

{txt}Akaike's information criterion and Bayesian information criterion

{hline 13}{c TT}{hline 63}
       Model {c |}          N   ll(null)  ll(model)      df        AIC        BIC
{hline 13}{c +}{hline 63}
{ralign 12:.}{col 14}{c |}{res}{col 16}       827{col 28} -4577.58{col 39}-4267.547{col 50}    40{col 58} 8615.095{col 69} 8803.807
{txt}{hline 13}{c BT}{hline 63}
{p 0 6 0 77}Note: BIC uses N = number of observations. See {helpb bic_note:{bind:[R] BIC note}}.{p_end}

{com}. 
. 
. *** COMPUTE Figure A1: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the HAZARD RATIO of APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}{c )-}. ****
. ** NOTE: IQ = 1.3670962 [0.9710589 - (-0.3960373)] for reduced sample N = 827 **
.  
. lincomest 1.soubinaryagency2nom#c.zloyalmedian*1.3670962, eform(hr)
{txt}Confidence interval for formula:
{res}1.soubinaryagency2nom#c.zloyalmedian*1.3670962

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}          _t{col 14}{c |}         hr{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}(1) {c |}{col 14}{res}{space 2} .5259256{col 26}{space 2} .1012944{col 37}{space 1}   -3.34{col 46}{space 3}0.001{col 54}{space 4} .3605629{col 67}{space 3} .7671276
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. matrix modelA22zloyal = r(table)
{txt}
{com}. mat list modelA22zloyal
{res}
{txt}modelA22zloyal[9,1]
               (1)
     b {res}  .52592564
{txt}    se {res}  .10129444
{txt}     z {res} -3.3363868
{txt}pvalue {res}  .00084875
{txt}    ll {res}  .36056291
{txt}    ul {res}  .76712764
{txt}    df {res}          .
{txt}  crit {res}   1.959964
{txt} eform {res}          1
{reset}
{com}. 
. 
. 
. **** COMPUTE Figure A2: Interquartile Increase Marginal Effect Change of Appointee Loyalty on the MEDIAN NUMBER OF DAYS OF APPOINTEE TENURE {c -(}PP − NPP Difference{c )-} {c -(}{c -(}4 [M1−M4] × 1 Horizontal Point Estimates and 95% CIs{c )-}.
. ** NOTE: IQ = 1.3670962 [0.9710589 - (-0.3960373)] for reduced sample N = 827 **
. 
. ** Re-Estimate Model A12  with 'manual' interaction variable **
. drop loyalppdiff
{txt}
{com}. generate loyalppdiff = soubinaryagency2nom*zloyalmedian
{txt}
{com}. 
. streg   zloyalmedian soubinaryagency2nom loyalppdiff  zpecompmedian  zmecompmedian   toplevel2   presagencyideolalign  presagencyideolopposed subagencydesign standaloneagencydesign  okstartsenpolarizationmean okstartfilipresdistance   okcrossover okstartpresapp okstartunemployment  i.okstartadyr i.sbagency reagan bush41 clinton bush43 if postemployment!=9999, distribution(weibull)hr vce(cluster sbagency)

         {txt}failure _d:  {res}singleadmin_service
   {txt}analysis time _t:  {res}okapptdur
{txt}note: 27.sbagency omitted because of collinearity
note: 57.sbagency omitted because of collinearity
note: 61.sbagency omitted because of collinearity

Fitting constant-only model:

Iteration 0:   log pseudolikelihood = {res} -973.7221
{txt}Iteration 1:   log pseudolikelihood = {res} -803.4444
{txt}Iteration 2:   log pseudolikelihood = {res} -799.2932
{txt}Iteration 3:   log pseudolikelihood = {res}-799.29246
{txt}Iteration 4:   log pseudolikelihood = {res}-799.29246

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-799.29246}  
Iteration 1:{space 3}log pseudolikelihood = {res:-580.92763}  
Iteration 2:{space 3}log pseudolikelihood = {res:-481.57732}  
Iteration 3:{space 3}log pseudolikelihood = {res:-480.33228}  
Iteration 4:{space 3}log pseudolikelihood = {res: -480.3297}  
Iteration 5:{space 3}log pseudolikelihood = {res: -480.3297}  
{res}
{txt}Weibull PH regression

No. of subjects      = {res}         827             {txt}Number of obs    =  {res}       827
{txt}No. of failures      = {res}         799
{txt}Time at risk         = {res}      821898
{col 49}{help j_robustsingular##|_new:Wald chi2(22)}{txt}{col 66}=  {res}         .
{txt}Log pseudolikelihood =   {res} -480.3297             {txt}Prob > chi2      =  {res}         .

{txt}{ralign 92:(Std. Err. adjusted for {res:41} clusters in sbagency)}
{hline 27}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 28}{c |}{col 40}    Robust
{col 1}                        _t{col 28}{c |} Haz. Ratio{col 40}   Std. Err.{col 52}      z{col 60}   P>|z|{col 68}     [95% Con{col 81}f. Interval]
{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}zloyalmedian {c |}{col 28}{res}{space 2}  1.35551{col 40}{space 2} .1530968{col 51}{space 1}    2.69{col 60}{space 3}0.007{col 68}{space 4} 1.086337{col 81}{space 3} 1.691379
{txt}{space 7}soubinaryagency2nom {c |}{col 28}{res}{space 2} 1.192036{col 40}{space 2} .2349135{col 51}{space 1}    0.89{col 60}{space 3}0.373{col 68}{space 4} .8101094{col 81}{space 3} 1.754021
{txt}{space 15}loyalppdiff {c |}{col 28}{res}{space 2} .6344307{col 40}{space 2} .0879921{col 51}{space 1}   -3.28{col 60}{space 3}0.001{col 68}{space 4} .4834228{col 81}{space 3} .8326092
{txt}{space 13}zpecompmedian {c |}{col 28}{res}{space 2} 1.039747{col 40}{space 2} .0888153{col 51}{space 1}    0.46{col 60}{space 3}0.648{col 68}{space 4} .8794635{col 81}{space 3} 1.229242
{txt}{space 13}zmecompmedian {c |}{col 28}{res}{space 2} .9894266{col 40}{space 2} .0681836{col 51}{space 1}   -0.15{col 60}{space 3}0.877{col 68}{space 4} .8644212{col 81}{space 3} 1.132509
{txt}{space 17}toplevel2 {c |}{col 28}{res}{space 2} .5466493{col 40}{space 2} .0568709{col 51}{space 1}   -5.81{col 60}{space 3}0.000{col 68}{space 4}  .445814{col 81}{space 3} .6702918
{txt}{space 6}presagencyideolalign {c |}{col 28}{res}{space 2} .4900248{col 40}{space 2} .0920658{col 51}{space 1}   -3.80{col 60}{space 3}0.000{col 68}{space 4}  .339074{col 81}{space 3} .7081768
{txt}{space 4}presagencyideolopposed {c |}{col 28}{res}{space 2} .4735479{col 40}{space 2} .0952725{col 51}{space 1}   -3.72{col 60}{space 3}0.000{col 68}{space 4}  .319236{col 81}{space 3}  .702451
{txt}{space 11}subagencydesign {c |}{col 28}{res}{space 2} 2.561785{col 40}{space 2}  .355723{col 51}{space 1}    6.77{col 60}{space 3}0.000{col 68}{space 4} 1.951403{col 81}{space 3}  3.36309
{txt}{space 4}standaloneagencydesign {c |}{col 28}{res}{space 2} 2.569049{col 40}{space 2} .6169177{col 51}{space 1}    3.93{col 60}{space 3}0.000{col 68}{space 4} 1.604609{col 81}{space 3} 4.113158
{txt}okstartsenpolarizationmean {c |}{col 28}{res}{space 2} 2.51e-10{col 40}{space 2} 2.55e-09{col 51}{space 1}   -2.18{col 60}{space 3}0.029{col 68}{space 4} 5.82e-19{col 81}{space 3} .1083446
{txt}{space 3}okstartfilipresdistance {c |}{col 28}{res}{space 2} 734.5842{col 40}{space 2} 1643.357{col 51}{space 1}    2.95{col 60}{space 3}0.003{col 68}{space 4} 9.157915{col 81}{space 3} 58923.23
{txt}{space 15}okcrossover {c |}{col 28}{res}{space 2}  .173548{col 40}{space 2} .0368997{col 51}{space 1}   -8.24{col 60}{space 3}0.000{col 68}{space 4} .1144031{col 81}{space 3} .2632703
{txt}{space 12}okstartpresapp {c |}{col 28}{res}{space 2} .9909597{col 40}{space 2} .0043801{col 51}{space 1}   -2.05{col 60}{space 3}0.040{col 68}{space 4} .9824119{col 81}{space 3} .9995819
{txt}{space 7}okstartunemployment {c |}{col 28}{res}{space 2} 1.134625{col 40}{space 2} .0997534{col 51}{space 1}    1.44{col 60}{space 3}0.151{col 68}{space 4}   .95503{col 81}{space 3} 1.347994
{txt}{space 26} {c |}
{space 15}okstartadyr {c |}
{space 24}2  {c |}{col 28}{res}{space 2}   1.6086{col 40}{space 2} .3748478{col 51}{space 1}    2.04{col 60}{space 3}0.041{col 68}{space 4} 1.018814{col 81}{space 3}  2.53981
{txt}{space 24}3  {c |}{col 28}{res}{space 2} 4.486051{col 40}{space 2} .9919216{col 51}{space 1}    6.79{col 60}{space 3}0.000{col 68}{space 4} 2.908392{col 81}{space 3} 6.919511
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 3.713647{col 40}{space 2} 1.177083{col 51}{space 1}    4.14{col 60}{space 3}0.000{col 68}{space 4} 1.995274{col 81}{space 3} 6.911919
{txt}{space 24}5  {c |}{col 28}{res}{space 2} 1.485375{col 40}{space 2} .3501628{col 51}{space 1}    1.68{col 60}{space 3}0.093{col 68}{space 4} .9357799{col 81}{space 3} 2.357755
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 3.355545{col 40}{space 2} .8024373{col 51}{space 1}    5.06{col 60}{space 3}0.000{col 68}{space 4} 2.099949{col 81}{space 3} 5.361885
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 6.121831{col 40}{space 2} 1.823601{col 51}{space 1}    6.08{col 60}{space 3}0.000{col 68}{space 4} 3.414454{col 81}{space 3} 10.97593
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 9.765367{col 40}{space 2} 3.703139{col 51}{space 1}    6.01{col 60}{space 3}0.000{col 68}{space 4} 4.644118{col 81}{space 3} 20.53402
{txt}{space 26} {c |}
{space 18}sbagency {c |}
{space 24}2  {c |}{col 28}{res}{space 2} 4.003403{col 40}{space 2} .8943871{col 51}{space 1}    6.21{col 60}{space 3}0.000{col 68}{space 4} 2.583837{col 81}{space 3} 6.202881
{txt}{space 24}3  {c |}{col 28}{res}{space 2}  2.63046{col 40}{space 2} .5517055{col 51}{space 1}    4.61{col 60}{space 3}0.000{col 68}{space 4} 1.743825{col 81}{space 3} 3.967897
{txt}{space 24}4  {c |}{col 28}{res}{space 2} 1.157946{col 40}{space 2} .2648505{col 51}{space 1}    0.64{col 60}{space 3}0.521{col 68}{space 4} .7396019{col 81}{space 3} 1.812921
{txt}{space 24}5  {c |}{col 28}{res}{space 2} .9276681{col 40}{space 2} .2525706{col 51}{space 1}   -0.28{col 60}{space 3}0.783{col 68}{space 4} .5440529{col 81}{space 3} 1.581773
{txt}{space 24}6  {c |}{col 28}{res}{space 2} 2.343992{col 40}{space 2} .5616963{col 51}{space 1}    3.55{col 60}{space 3}0.000{col 68}{space 4} 1.465483{col 81}{space 3} 3.749139
{txt}{space 24}7  {c |}{col 28}{res}{space 2} 2.546775{col 40}{space 2} .6197499{col 51}{space 1}    3.84{col 60}{space 3}0.000{col 68}{space 4} 1.580714{col 81}{space 3} 4.103248
{txt}{space 24}8  {c |}{col 28}{res}{space 2} 3.278956{col 40}{space 2} .7106303{col 51}{space 1}    5.48{col 60}{space 3}0.000{col 68}{space 4}  2.14417{col 81}{space 3} 5.014318
{txt}{space 24}9  {c |}{col 28}{res}{space 2} 3.023856{col 40}{space 2}   .68525{col 51}{space 1}    4.88{col 60}{space 3}0.000{col 68}{space 4} 1.939396{col 81}{space 3} 4.714719
{txt}{space 23}11  {c |}{col 28}{res}{space 2} 5.231217{col 40}{space 2}  1.40726{col 51}{space 1}    6.15{col 60}{space 3}0.000{col 68}{space 4} 3.087588{col 81}{space 3} 8.863108
{txt}{space 23}12  {c |}{col 28}{res}{space 2} 2.551862{col 40}{space 2} .3531373{col 51}{space 1}    6.77{col 60}{space 3}0.000{col 68}{space 4} 1.945648{col 81}{space 3} 3.346956
{txt}{space 23}13  {c |}{col 28}{res}{space 2} 2.227556{col 40}{space 2} .4230548{col 51}{space 1}    4.22{col 60}{space 3}0.000{col 68}{space 4} 1.535215{col 81}{space 3} 3.232122
{txt}{space 23}14  {c |}{col 28}{res}{space 2} 3.446004{col 40}{space 2} .8085902{col 51}{space 1}    5.27{col 60}{space 3}0.000{col 68}{space 4}  2.17563{col 81}{space 3} 5.458164
{txt}{space 23}15  {c |}{col 28}{res}{space 2} 2.254484{col 40}{space 2} .4518583{col 51}{space 1}    4.06{col 60}{space 3}0.000{col 68}{space 4} 1.522102{col 81}{space 3} 3.339262
{txt}{space 23}16  {c |}{col 28}{res}{space 2}  .818045{col 40}{space 2} .1489603{col 51}{space 1}   -1.10{col 60}{space 3}0.270{col 68}{space 4} .5725051{col 81}{space 3} 1.168894
{txt}{space 23}17  {c |}{col 28}{res}{space 2} 1.473292{col 40}{space 2} .1521614{col 51}{space 1}    3.75{col 60}{space 3}0.000{col 68}{space 4} 1.203308{col 81}{space 3} 1.803851
{txt}{space 23}18  {c |}{col 28}{res}{space 2} 2.770528{col 40}{space 2} .6569975{col 51}{space 1}    4.30{col 60}{space 3}0.000{col 68}{space 4} 1.740646{col 81}{space 3} 4.409759
{txt}{space 23}19  {c |}{col 28}{res}{space 2} .7989486{col 40}{space 2} .1260205{col 51}{space 1}   -1.42{col 60}{space 3}0.155{col 68}{space 4} .5864842{col 81}{space 3} 1.088382
{txt}{space 23}20  {c |}{col 28}{res}{space 2} .1994632{col 40}{space 2}  .048436{col 51}{space 1}   -6.64{col 60}{space 3}0.000{col 68}{space 4} .1239264{col 81}{space 3} .3210418
{txt}{space 23}21  {c |}{col 28}{res}{space 2} .8171255{col 40}{space 2} .0868843{col 51}{space 1}   -1.90{col 60}{space 3}0.058{col 68}{space 4} .6634086{col 81}{space 3}  1.00646
{txt}{space 23}22  {c |}{col 28}{res}{space 2} .3499687{col 40}{space 2}  .106723{col 51}{space 1}   -3.44{col 60}{space 3}0.001{col 68}{space 4} .1925109{col 81}{space 3}  .636214
{txt}{space 23}23  {c |}{col 28}{res}{space 2} .7815934{col 40}{space 2} .1780763{col 51}{space 1}   -1.08{col 60}{space 3}0.279{col 68}{space 4} .5000866{col 81}{space 3} 1.221565
{txt}{space 23}24  {c |}{col 28}{res}{space 2}  .226177{col 40}{space 2} .0833266{col 51}{space 1}   -4.03{col 60}{space 3}0.000{col 68}{space 4} .1098638{col 81}{space 3} .4656316
{txt}{space 23}25  {c |}{col 28}{res}{space 2} 1.456883{col 40}{space 2}   .24361{col 51}{space 1}    2.25{col 60}{space 3}0.024{col 68}{space 4} 1.049767{col 81}{space 3} 2.021887
{txt}{space 23}26  {c |}{col 28}{res}{space 2} .7838878{col 40}{space 2} .1251802{col 51}{space 1}   -1.52{col 60}{space 3}0.127{col 68}{space 4} .5732238{col 81}{space 3} 1.071972
{txt}{space 23}27  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}28  {c |}{col 28}{res}{space 2} 1.338212{col 40}{space 2} .1301763{col 51}{space 1}    2.99{col 60}{space 3}0.003{col 68}{space 4} 1.105919{col 81}{space 3} 1.619298
{txt}{space 23}29  {c |}{col 28}{res}{space 2} 4.712038{col 40}{space 2} 1.334603{col 51}{space 1}    5.47{col 60}{space 3}0.000{col 68}{space 4}  2.70471{col 81}{space 3} 8.209125
{txt}{space 23}30  {c |}{col 28}{res}{space 2} 2.040479{col 40}{space 2} .5944902{col 51}{space 1}    2.45{col 60}{space 3}0.014{col 68}{space 4} 1.152752{col 81}{space 3} 3.611838
{txt}{space 23}50  {c |}{col 28}{res}{space 2} 1.885441{col 40}{space 2} .3484628{col 51}{space 1}    3.43{col 60}{space 3}0.001{col 68}{space 4}  1.31249{col 81}{space 3} 2.708508
{txt}{space 23}51  {c |}{col 28}{res}{space 2} 2.843411{col 40}{space 2} .7553074{col 51}{space 1}    3.93{col 60}{space 3}0.000{col 68}{space 4} 1.689395{col 81}{space 3} 4.785727
{txt}{space 23}52  {c |}{col 28}{res}{space 2} 1.457568{col 40}{space 2} .4820135{col 51}{space 1}    1.14{col 60}{space 3}0.255{col 68}{space 4} .7623226{col 81}{space 3} 2.786883
{txt}{space 23}53  {c |}{col 28}{res}{space 2} 1.184579{col 40}{space 2}  .162209{col 51}{space 1}    1.24{col 60}{space 3}0.216{col 68}{space 4} .9057447{col 81}{space 3} 1.549253
{txt}{space 23}54  {c |}{col 28}{res}{space 2} 1.527979{col 40}{space 2} .2831542{col 51}{space 1}    2.29{col 60}{space 3}0.022{col 68}{space 4} 1.062622{col 81}{space 3} 2.197131
{txt}{space 23}55  {c |}{col 28}{res}{space 2} .9879224{col 40}{space 2} .3516849{col 51}{space 1}   -0.03{col 60}{space 3}0.973{col 68}{space 4} .4917093{col 81}{space 3} 1.984894
{txt}{space 23}56  {c |}{col 28}{res}{space 2} .9028743{col 40}{space 2} .3395618{col 51}{space 1}   -0.27{col 60}{space 3}0.786{col 68}{space 4} .4320153{col 81}{space 3} 1.886929
{txt}{space 23}57  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 23}58  {c |}{col 28}{res}{space 2} 1.089079{col 40}{space 2}  .347013{col 51}{space 1}    0.27{col 60}{space 3}0.789{col 68}{space 4} .5832322{col 81}{space 3} 2.033657
{txt}{space 23}59  {c |}{col 28}{res}{space 2} .3580341{col 40}{space 2} .0862945{col 51}{space 1}   -4.26{col 60}{space 3}0.000{col 68}{space 4} .2232365{col 81}{space 3}  .574227
{txt}{space 23}60  {c |}{col 28}{res}{space 2} .9122313{col 40}{space 2} .1313774{col 51}{space 1}   -0.64{col 60}{space 3}0.524{col 68}{space 4} .6878867{col 81}{space 3} 1.209743
{txt}{space 23}61  {c |}{col 28}{res}{space 2}        1{col 40}{txt}  (omitted)
{space 26} {c |}
{space 20}reagan {c |}{col 28}{res}{space 2} .0658078{col 40}{space 2}  .062314{col 51}{space 1}   -2.87{col 60}{space 3}0.004{col 68}{space 4} .0102865{col 81}{space 3} .4210053
{txt}{space 20}bush41 {c |}{col 28}{res}{space 2} .1617427{col 40}{space 2} .0992425{col 51}{space 1}   -2.97{col 60}{space 3}0.003{col 68}{space 4} .0485895{col 81}{space 3} .5384025
{txt}{space 19}clinton {c |}{col 28}{res}{space 2}  .587791{col 40}{space 2}  .309446{col 51}{space 1}   -1.01{col 60}{space 3}0.313{col 68}{space 4} .2094609{col 81}{space 3} 1.649464
{txt}{space 20}bush43 {c |}{col 28}{res}{space 2} .2162847{col 40}{space 2} .1597535{col 51}{space 1}   -2.07{col 60}{space 3}0.038{col 68}{space 4} .0508517{col 81}{space 3} .9199105
{txt}{space 21}_cons {c |}{col 28}{res}{space 2} .0001781{col 40}{space 2} .0009364{col 51}{space 1}   -1.64{col 60}{space 3}0.101{col 68}{space 4} 5.96e-09{col 81}{space 3} 5.319039
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}/ln_p {c |}{col 28}{res}{space 2} .9867003{col 40}{space 2} .0303356{col 51}{space 1}   32.53{col 60}{space 3}0.000{col 68}{space 4} .9272437{col 81}{space 3} 1.046157
{txt}{hline 27}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         p {c |}{col 28}{res}{space 2} 2.682369{col 40}{space 2} .0813712{col 68}{space 4} 2.527533{col 81}{space 3}  2.84669
{txt}                       1/p {c |}{col 28}{res}{space 2} .3728048{col 40}{space 2} .0113092{col 68}{space 4} .3512852{col 81}{space 3} .3956427
{txt}{hline 27}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline hazard{txt}.{p_end}

{com}. 
. estimates store modela22
{txt}
{com}. 
. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9710589))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       827
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9710589}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 929.2447{col 26}{space 2}  27.3777{col 37}{space 1}   33.94{col 46}{space 3}0.000{col 54}{space 4} 875.5854{col 67}{space 3}  982.904
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1171.782{col 26}{space 2} 58.41095{col 37}{space 1}   20.06{col 46}{space 3}0.000{col 54}{space 4} 1057.299{col 67}{space 3} 1286.266
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ** Generate Differential Predicted Median Survival Time of Senate Committee Stage of Confirmation Process -- Based on Interquartile Differential [corresponding to Differential Marginal Hazard Ratio Estimates] **
. 
. margins, predict(median time) at(loyalppdiff=(-0.3960373 0.9710589))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       827
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.3960373}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}.9710589}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     9.76{col 38}{space 2}   0.0018
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2} 242.5377{col 26}{space 2} 77.62192{col 37}{space 5} 90.40154{col 51}{space 3} 394.6739
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelA22azloyal = r(table)
{txt}
{com}. mat list modelA22azloyal
{res}
{txt}modelA22azloyal[9,1]
            r2vs1.
              _at
     b {res} 242.53771
{txt}    se {res} 77.621923
{txt}     z {res} 3.1246032
{txt}pvalue {res} .00178045
{txt}    ll {res} 90.401539
{txt}    ul {res} 394.67389
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. 
. estimates restore modela22
{txt}(results {stata estimates replay modela22:modela22} are active now)

{com}. 
. margins, predict(median time) at(loyalppdiff=(-.6691019 1.733512))
{res}
{txt}Predictive margins{col 49}Number of obs{col 67}= {res}       827
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6691019}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.733512}{p_end}
{p2colreset}{...}

{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}     Margin{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}_at {c |}
{space 10}1  {c |}{col 14}{res}{space 2} 887.1822{col 26}{space 2} 37.20598{col 37}{space 1}   23.85{col 46}{space 3}0.000{col 54}{space 4} 814.2598{col 67}{space 3} 960.1046
{txt}{space 10}2  {c |}{col 14}{res}{space 2} 1333.578{col 26}{space 2} 117.6758{col 37}{space 1}   11.33{col 46}{space 3}0.000{col 54}{space 4} 1102.938{col 67}{space 3} 1564.219
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. margins, predict(median time) at(loyalppdiff=(-.6691019 1.733512))  contrast(atcontrast(r))
{res}
{txt}Contrasts of predictive margins{col 49}Number of obs{col 67}= {res}       827
{txt}{col 1}Model VCE{col 14}: {res}Robust

{txt}{p2colset 1 14 16 2}{...}
{p2col:Expression}:{space 1}{res:Predicted median _t, predict(median time)}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:1._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 2}-.6691019}{p_end}
{p2colreset}{...}

{txt}{p2colset 1 14 16 2}{...}
{p2col:2._at}:{space 1}{res:{txt:loyalppdiff}{space 5}{txt:=} {space 3}1.733512}{p_end}
{p2colreset}{...}

{res}{col 1}{text}{hline 13}{c TT}{hline 11}{hline 12}{hline 11}
{col 14}{text}{c |}         df{col 26}        chi2{col 38}     P>chi2
{res}{col 1}{text}{hline 13}{c +}{hline 11}{hline 12}{hline 11}
{space 9}_at {res}{col 14}{text}{c |}{result}{space 2}        1{col 26}{space 3}     8.88{col 38}{space 2}   0.0029
{col 1}{text}{hline 13}{c BT}{hline 11}{hline 12}{hline 11}
{res}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 14}{c |}{col 26} Delta-method
{col 14}{c |}   Contrast{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{space 9}_at {c |}
{space 3}(2 vs 1)  {c |}{col 14}{res}{space 2}  446.396{col 26}{space 2} 149.8195{col 37}{space 5} 152.7553{col 51}{space 3} 740.0368
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}
{res}{txt}
{com}. 
. matrix modelA22bzloyal = r(table)
{txt}
{com}. mat list modelA22bzloyal
{res}
{txt}modelA22bzloyal[9,1]
            r2vs1.
              _at
     b {res} 446.39604
{txt}    se {res} 149.81947
{txt}     z {res} 2.9795596
{txt}pvalue {res} .00288663
{txt}    ll {res} 152.75527
{txt}    ul {res} 740.03681
{txt}    df {res}         .
{txt}  crit {res}  1.959964
{txt} eform {res}         0
{reset}
{com}. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. 
. *Figure A1
. 
. matrix pointmodel = model2zloyal[1,1], model4zloyal[1,1], model4zloyal[7,1], modelA11zloyal[1,1], modelA21zloyal[1,1], modelA22zloyal[1,1]
{txt}
{com}. 
. *
. matrix cimodel = (model2zloyal[5,1], model4zloyal[5,1], model4zloyal[7,1], modelA11zloyal[5,1], modelA21zloyal[5,1], modelA22zloyal[5,1] \ model2zloyal[6,1], model4zloyal[6,1], model4zloyal[7,1], modelA11zloyal[6,1], modelA21zloyal[6,1], modelA22zloyal[6,1])
{txt}
{com}. 
. coefplot (matrix(pointmodel), ci((cimodel))), grid(none) xline(1, lcolor(red%40) lpattern(dash)) xtitle("Hazard Ratio", size(vsmall) margin(t=2)) ylabel(1 `""Presidential Loyalty x Policy Priority Agencies" "Model 2: Cox Model""'  2 `""Presidential Loyalty x Policy Priority Agencies" "Model 4: Weibull Model""'  3 " " 4 `""Presidential Loyalty x Policy Priority Agencies" "Model A1: Gompertz Model""' 5 `""Presidential Loyalty x Policy Priority Agencies" "Model A2.1: Competing-Risks Model""' 6 `""Presidential Loyalty x Policy Priority Agencies" "Model A2.2: Cox Model""', labsize(vsmall) noticks) mlabel format(%9.3f) mlabposition(12) mlabsize(vsmall) xlabel(0(1)2, angle(0) labsize(vsmall) format(%9.1f)) msymbol(o) mcolor(black) msize(small) title("FIGURE A1", size(small)) ciopts(lcolor(black)) legend(off) subtitle("Marginal Differential Effect of Presidential Loyalty on Appointee Tenure Hazard" "Alternative Parametric Hazards & Data Designs" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(vsmall))
{res}{txt}(pointmodel: b missing for some coefficients)
{txt}(pointmodel: CI1 missing for some coefficients)
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureA1.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureA1.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureA1.gph saved)

{com}. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. *Figure A2
. 
. matrix pointmodel1 = model4azloyal[1,1], model4bzloyal[1,1], model4bzloyal[7,1], modelA11azloyal[1,1], modelA11bzloyal[1,1], modelA12azloyal[1,1], modelA12bzloyal[1,1]
{txt}
{com}. 
. 
. *
. matrix cimodel1 = (model4azloyal[5,1], model4bzloyal[5,1], model4bzloyal[7,1], modelA11azloyal[5,1], modelA11bzloyal[5,1], modelA12azloyal[5,1], modelA12bzloyal[5,1] \ model4azloyal[6,1], model4bzloyal[6,1], model4bzloyal[7,1], modelA11azloyal[6,1], modelA11bzloyal[6,1], modelA12azloyal[6,1], modelA12bzloyal[6,1])
{txt}
{com}. 
. coefplot (matrix(pointmodel1), ci((cimodel1))), grid(none) xline(0, lcolor(red%40) lpattern(dash)) xtitle("Predicted Number of Days", size(vsmall) margin(t=2)) ylabel(1 `""Presidential Loyalty x Policy Priority Agencies" "Model 4: Interquartile Change""' 2 `""Presidential Loyalty x Policy Priority Agencies" "Model 4: Interdecile Change""' 3 " " 4 `""Presidential Loyalty x Policy Priority Agencies" "Model A1.4: Interquartile Change""' 5 `""Presidential Loyalty x Policy Priority Agencies" "Model A1.4: Interdecile Change""' 6 `""Presidential Loyalty x Policy Priority Agencies" "Model A2.4: Interquartile Change""' 7 `""Presidential Loyalty x Policy Priority Agencies" "Model A2.4: Interdecile Change""', labsize(vsmall) noticks) mlabel format(%9.0f) mlabposition(12) mlabsize(vsmall) xlabel(0(100)800, angle(0) labsize(vsmall) format(%9.0f))   msymbol(o) mcolor(black) msize(small) title("FIGURE A2", size(small)) ciopts(lcolor(black)) legend(off) subtitle("Marginal Differential Effect of Presidential Loyalty Predicting Median Appointee Tenure" "Alternative Parametric Hazards & Data Designs" "[Policy Priority Agencies versus Non-Policy Priority Agencies]", size(vsmall))
{res}{txt}(pointmodel1: b missing for some coefficients)
{txt}(pointmodel1: CI1 missing for some coefficients)
{res}{txt}
{com}. 
. graph save "Graph" "C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureA2.gph", replace
{txt}(note: file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureA2.gph not found)
{res}{txt}(file C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Graphics\FigureA2.gph saved)

{com}. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. *Kolmogorov-Smirnov Nonparametric Equality of Distributions Test
. ksmirnov zloyalmedian, by(soubinaryagency2nom)

{txt}Two-sample Kolmogorov-Smirnov test for equality of distribution functions

 Smaller group       D       P-value  
 {hline 35}
 0:                {res}  0.0433    0.491
{txt} 1:                {res} -0.0563    0.300
{txt} Combined K-S:     {res}  0.0563    0.584

{txt}Note: Ties exist in combined dataset;
      there are 601 unique values out of 860 observations.

{com}. 
. 
. 
. ***************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************************
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Jason\Dropbox\Jason Byers\Co-Authored Projects\Projects with George Krause\Krause Projects\Confirmation Dynamics Project\Appointee Tenure Project\Jason Byers\March 2023\DART (PRQ)\Output\Hardwiring Committment.APPENDIX A.04-21-2023.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}22 Apr 2023, 09:51:27
{txt}{.-}
{smcl}
{txt}{sf}{ul off}